WO2010077997A2 - Method and apparatus for determining heart rate variability using wavelet transformation - Google Patents

Method and apparatus for determining heart rate variability using wavelet transformation Download PDF

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WO2010077997A2
WO2010077997A2 PCT/US2009/068336 US2009068336W WO2010077997A2 WO 2010077997 A2 WO2010077997 A2 WO 2010077997A2 US 2009068336 W US2009068336 W US 2009068336W WO 2010077997 A2 WO2010077997 A2 WO 2010077997A2
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heart
body
ecg waveform
electronic signals
signal
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PCT/US2009/068336
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French (fr)
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WO2010077997A3 (en
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Kayvan Najarian
Kevin Ward
David Andre
Nisarg Vyas
Eric Teller
John M. Stivoric
Jonathan Farringdon
Scott Boehmke
James Gasbarro
Soo-Yeon Ji
Abed Al Raoof
Raymond Pelletier
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Bodymedia, Inc.
Virginia Commonwealth University
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Priority to US12280408P priority Critical
Priority to US61/122,804 priority
Priority to US16728709P priority
Priority to US61/167,287 priority
Priority to PCT/US2009/006234 priority patent/WO2010065067A1/en
Priority to USPCT/US2009/006234 priority
Application filed by Bodymedia, Inc., Virginia Commonwealth University filed Critical Bodymedia, Inc.
Priority claimed from US13/140,165 external-priority patent/US20120123232A1/en
Publication of WO2010077997A2 publication Critical patent/WO2010077997A2/en
Publication of WO2010077997A3 publication Critical patent/WO2010077997A3/en

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405Determining heart rate variability
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Measuring bioelectric signals of the body or parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/0452Detecting specific parameters of the electrocardiograph cycle
    • A61B5/0472Detecting abnormal QRS complex, e.g. widening
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/41Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • A61B5/412Detecting or monitoring sepsis
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7253Details of waveform analysis characterised by using transforms
    • A61B5/726Details of waveform analysis characterised by using transforms using Wavelet transforms
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021Measuring pressure in heart or blood vessels
    • A61B5/022Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
    • A61B5/02208Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers using the Korotkoff method
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/22Ergometry; Measuring muscular strength or the force of a muscular blow
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813Specially adapted to be attached to a specific body part
    • A61B5/6824Arm or wrist
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7239Details of waveform analysis using differentiation including higher order derivatives
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device

Abstract

The present invention relates to advanced signal processing methods including digital wavelet transformation to analyze heart-related electronic signals and extract features that can accurately identify various states of the cardiovascular system. The invention may be utilized to estimate the extent of blood volume loss, distinguish blood volume loss from physiological activities associated with exercise, and predict the presence and extent of cardiovascular disease in general.

Description

TITLE

METHOD AND APPARATUS FOR DETERMINING HEART RATE VARIABILITY USING WAVELET TRANSFORMATION

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation-in-part of International Application No.

PCT/US09/06234, titled "Method and Apparatus for Determining Critical Care Parameters," filed November 20, 2009. PCT/US09/06234 is a continuation-in-part of U.S. Application Serial No. 11/928,302, filed on October 30, 2007, which is a continuation of U.S. application Serial No. 10/940,889, filed Sep. 13, 2004, issued as U.S. Patent No. 7,502,643. U.S. application Serial No. 10/940,889 claims the benefit of U.S. Provisional Application Serial No.60/502,764, filed Sep. 12, 2003; U.S. Provisional Application Serial No. 60/510,013, filed Oct. 9, 2003; and U.S. Provisional Application Serial No. 60/555,280, filed Mar. 22, 2004. PCT/US09/06234 is also a continuation-in- part of co-pending U.S. Patent Application Serial No. 10/940,214, filed September 13, 2004, which is a continuation in part of co-pending U.S. application Ser. No. 10/638,588, filed Aug. 11, 2003, which is a continuation of U.S. application Ser. No. 09/602,537, filed Jun. 23, 2000, issued as U.S. Patent No. 6,605,038, which is a continuation-in-part of co-pending U.S. application Ser. No. 09/595,660, filed Jun. 16, 2000, and which claims the benefit of U.S. Provisional Application No. 60/502,764 filed on Sep. 13, 2003 and U.S. Provisional Application No. 60/555,280 filed on Mar. 22, 2004. PCT/US09/06234 is also a continuation-in-part of U.S. Patent Application Serial No. 10/682,293, filed October 9, 2003, which claims the benefit of U.S. Provisional Application No. 60/417,163 filed on October 9, 2002. PCT/US09/06234 also claims priority to U.S. Provisional Application No. 61/116,364, filed on November 20, 2008.

[0002] This application also claims priority to U.S. Provisional Application No.

61/167,287, titled "Detection of P, QRS, and T Components of ECG Using Wavelet Transformation," filed April 7, 2009 and U.S. Provisional Application No. 61/122,804, titled "Heart Rate Variability, Vital Sign and Physiologic Analysis Using Wavelet Transformation and Machine Learning," filed December 16, 2008. Each patent application referenced above is incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH [0003] This invention was made with Government support under United States Army

Grant 05-0033-02. The Government may retain certain rights in the invention.

FIELD OF THE INVENTION

[0004] The present invention relates to advanced signal processing methods including discrete wavelet transformation to analyze heart-related electronic signals and extract features that can accurately identify various states of the cardiovascular and nervous system. Discovery of cardiac diseases, heart rate variability, and estimation of the amount of blood volume loss during hemorrhage shock are among many applications for which the technology may be used.

BACKGROUND OF THE INVENTION

[0005] The importance of ECG analysis is based upon, in part, the fact that it can be utilized to identify many cardiac conditions such as ventricular fibrillation, arrhythmia, atria abnormality, and cardiac infarction. Several clinical details are encapsulated as intervals and amplitudes into ECG (see Fig. 39). Many algorithms have been developed in order to interpret these signals and extract conclusory information. The most significant clinical aspects of an ECG signal are the P, Q, R, S, and T waves. Sometimes, a sixth wave (U) may follow the T, Q, R, and S features may be grouped together to form the QRS-complex, which plays an important role in determining the heart rate. The QRS-complex may also be used to detect P and T waves not only because of its proximity relative to the P and T waves, in the signal, but also its high amplitude, which makes it easy to detect. [0006] The P wave lasts about 0.08s and results from movement of the depolarization wave from the sinoatrial (SA) node, the impulse generating tissue located in the right atrium of the heart, through the atria. Approximately 0.10s after the P wave begins, the atria contract. [0007] The large QRS complex results from ventricular depolarization and precedes ventricular contraction. It has a complicated shape because the paths of the depolarization waves through the ventricular septum change continuously, producing corresponding changes in current direction. Additionally, the time required for each ventricle to depolarize depends on its size relative to the other ventricle. Average duration of the QRS complex is 0.08s.

[0008] The T wave is caused by ventricular repolarization and typically lasts about

0.16s. Repolarization is slower than depolarization, and as a result the T wave has a lower wavelength out and has a lower amplitude (height) than the QRS wave. Because atrial repolarization takes place during the period of ventricular excitation, the wave representing atrial repolarization is normally obscured by the large QRS complex being recorded at the same time. [0009] Based on several previous studies, the ability automate Electrocardiogram (ECG) signals analysis is a potentially useful method to detect the most important clinical details in an ECG signal; P, Q, R, S and T waves. Many previous studies have developed QRS complex, P and T waves detection methods. As for QRS complex detection, Kόhler et al have an extensive review for QRS detection methods. Acr presents a new system for the classification of ECG beats by using a fast least square support vector machine (LSSVM). Ghongade and Ghatol focus on various schemes for extracting the useful features of the ECG signals for use with artificial neural networks. Pahlm and Sδrnmo show a general scheme for non-syntactic QRS detector in which linear filtering, followed by a nonlinear transformation is carried out as a preprocessing step, followed by one or more decision rules. Bragge et al present a model based on a high-resolution QRS detection algorithm which is suitable for sparsely sampled ECG recordings. Zong et al discuss a novel algorithm to detect onset and duration of QRS complexes. Alvarado et al use continuous wavelet transformation (CWT) with splines to detect characteristic points of QRS and T waves. He Chen and SW Chen have designed a real-time QRS detection algorithm based on a simple moving average filter. Pan and Tompkins present a real-time algorithm for detection of the QRS complexes of ECG signals based upon digital analysis of slope, amplitude, and width. Gutierrez et al have developed an on-line QRS detection algorithm; the algorithm is based on a Haar wavelet and implemented as a recursive filter. Visinescu et al have developed an automatic QRS detection algorithm based on a wavelet pre-ftlter and an adaptive threshold technique, where the QRS complexes are identified by computing the first derivative of the signal and applying a set of adaptive thresholds that are not limited to a strict range.

[0010] For P and T Detection, Li et al discuss an algorithm based on wavelet transform

(WT) for detecting ECeG characteristic points. This study states that QRS complex is distinguished from high P or T waves, noise, baseline drift and artifacts by the multiscale feature of WTs. Martenz et al present a robust single-lead ECG delineation system which was developed and evaluated based on WT. Botter et al use a neural network with asymmetric basis functions to extract the features of P waves. Sovilj et al use a multistage methodology, enabled by WT, to delineate the ECG signal and develop a sensitive and reliable P-wave detector. Vila et al present a new TU complex detection and characterization algorithm that consists of two stages; mathematical modeling of the ECG segment after QRS complex, and classic threshold comparison technique. Strumillo proposes nested median filtering for detecting T-wave offset in ECG. Carlson et al have developed a method to discriminate between P wave morphology with intermittent atrial fibrillation and normal ones. Literature shows enormous methods for ECG components detection, however, it is sparse for P and T wave detection.

[0011] Many prior studies have focused only on detection of the QRS-complex because

P and T waves are sparse and harder to isolate from the signal. The significance of ECG analysis is based upon its utility or the identification of cardiac conditions such as ventricular fibrillation, arrhythmia, atria abnormality, and cardiac infarction. Additionally, systemic illnesses and injuries and their severity such as hemorrhage, infection, and brain injury may be identified. Currently, Wavelet Transformation (WT) and mathematical modeling of the ECG segment and threshold are being widely studied and advocated as a means to detect the ECG components. Unfortunately, mathematical modelings of ECG segment and threshold methods are sensitive to noise and baseline drift artifacts exist in the signal which can unexpectedly affect the detection. Although WT methods currently exist in the literature and are transparent to noise and baseline drift artifacts, they are time consuming and require more powerful computational devices. This is problematic, given the utility of WT in ECG analysis for detection of its components more accurately and effectively in terms of both speed and memory requirements.

[0012] Traumatic injury is the leading cause of death for individuals under age 44 in the

United States. Overall, trauma results in approximately 150,000 deaths per year, and severe hypovolemia due to hemorrhage is a major factor in nearly half of those deaths. Acute traumatic shock resulting in tissue injury and hemorrhage remains the primary cause of death on the battlefield, and is also a leading cause of death in civilian trauma. Hemorrhage shock is the most critical of life-threatening battle injuries; in one study of the Israeli military, 96% (351 out of 337) patient fatalities occurred in the first four hours, typically due to blood loss . Both these rapid deaths and many complications associated with the injury result from a lack of appropriate surgical attention and limited evacuation facilities in the field. Moreover, the likelihood of death depends on a number of factors, such as the severity of hemorrhagic shock, the time until rescue and the type of treatment provided.

[0013] Advanced Trauma Life Support guidelines have proven a successful treatment in civilian trauma. However, the treatment of battlefield injuries is more challenging and fatalities within the first hour of wounding are highly dependent on battlefield conditions. These conditions include the availability of medical personal and their skill and experience; limitations of medical equipment; and delay in transit to the nearest medical facility [. Field treatment of injuries has thus been a priority research issue. The current guidelines may be significantly improved by continuous patient observation, based on biomedical signals that can aid in early detection of severe blood loss. Therefore, the most important factors in caring for trauma patients in the field are appropriate training of medical personnel and sufficient preparation for environmental conditions. Another crucial factor in treatment of hemorrhagic shock is early identification of the source of bleeding and fluid resuscitation. However, the potential risks and benefits of early fluid resuscitation have been studied previously, with the conclusion that careful evaluation is required before changes are made to the established treatment methods for trauma patients.

[0014] It is well known that bio-signal time series are stochastic; however these time series have recently been identified as fractals generated by scaling phenomena. This novel approach is justified by the fact that physiological time series fluctuate in an irregular and complex manner in response to the dynamics of the entire biological system under study. One example of a physiological time series consists of the beat-to-beat intervals of the human heart, also known as the heart rate variability (HRV) time series. HRV is a non-invasive measurement of cardiovascular autonomic regulation. Recently, analysis of HRV has become a popular non-invasive tool for assessing activity of the autonomic nervous system. Monitoring the heart beat fluctuations observed in HRV appears to provide valuable information concerning cardiovascular and neurological diseases, as well as physical and mental stress. Heart variability in cardiovascular activity, such as RR interval, has been widely studied as a measure of cardiovascular function that can be used in both risk estimation and diagnosis of cardiac events.

[0015] There are two main traditional approaches for HRV analysis; time domain analysis of HRV for standard deviation of normal to normal intervals (SDNN), and frequency domain analysis for power spectrum density (PSD) using simple electrocardiogram (ECG). Previous studies have demonstrated that PSD analysis is a good non-invasive tool for examination of the cardiovascular system and it is currently the most popular linear technique used for studying BRV signals. PSD analysis provides three bands: high frequency (HF: 0.15-0.5 Hz), low frequency (LF: 0.04- 0.15 Hz), and very low frequency (VLF: 0.0033-0.04 Hz). However, PSD estimation methods are unsuitable for analyzing series whose characteristics change rapidly [. Also, spectral analysis of the residual ECG is sometimes difficult due to a low signal-to-noise ratio (SNR). These difficulties are worsened when time-frequency analysis with a short duration time window is used. [0016] The importance of biological time series analysis in describing complex patterns is well known. The nonlinear dynamical techniques are used based on the concept of chaos theory and have been applied to many areas, including medicine and biology. Thus, the physiological phenomena of HRV has been characterized by fractal properties and prior studies have emphasized fractal dimension (FD) analysis; a useful tool in the identification of complex biological systems under different conditions . It is also known FD analysis can then reliably identify heart disease, as the irregularity of HRV causes abnormal cases to have greater fractal complexity than and normal cases and that the FD measure may be significantly more effective than traditional measurement such as PSD.

[0017] Wavelet transform is a very promising technique for time-frequency analysis, providing several features not supported by Fourier transformation analysis. Since the combination of time and frequency resolution makes wavelet transform potentially very valuable, it is currently used for many practical applications in the field of biology and medicine. In particular, wavelet transform is well suited to local analysis of fast time varying and non-regular signals. Stiles et. al advocated that wavelet transform of the ECG signal offers advantages in detecting features of clinical significance that may not be reveal in existing methods.

[0018] Convertino and Stevens suggest that LBNP is a useful technique to study cardiovascular activity and hemodynamic effects associated with severe hemorrhage shock in humans, in particular in combat settings. Also, Cook et al states that LBNP is a useful model to simulate acute hemorrhage in humans, considering the fact that physiological response to hemorrhage and LBNP are similar.

[0019] It has been suggested that HRV becomes lower and more persistent with an increase in negative pressure. LBNP has been used for studying cardiovascular adjustments and may prove useful in the detection of hemorrhage shock in humans, especially in military applications. Thus, comparison between physiological response of LBNP and blood loss have demonstrated that some amount of blood loss and LBNP cause a similar physiological reaction. Cooke studied the physiological response to severe blood loss and stated that LBNP might be helpful in the study of acute hemorrhage. He also suggests PSD analysis may be a useful tool to extract valuable information from the HRV. Recently, Cooke et. al found that measure of high frequency to low frequency (HFILF) has a significant difference between trauma patients who alive and dead. [0020] Until recently it appeared that field use of HRV might prove to be valuable as a remote triage tool as a means to assess and treat multiple casualties. This was attractive since it could have potentially obviated the need for collection and analysis of other signals including blood pressure. However, several studies of physical activity such as exercise have also been conducted. Heidi et. al [Heidi 2000] studied the heart activity during different states, and found that R-R intervals decrease significantly during exercise and other vigorous activity. Rickards et al found that the measure of high frequency to low frequency (HF/LF) alone may not be enough to differentiate between LBNP and physical activity even though the measure has the potential different between normal and disease subject. Therefore, the ability to differentiate heart rate changes from blood loss due to wounding and heart rate changes due to activity have been inconclusive.

[0021] Based on several previous studies, it has been reported that studying heart rate variability (HRV) is a potentially useful non-invasive method to detect central volume loss and injury-illness severity in critically ill and injured subjects. Hemorrhagic shock (HS) can be a lethal consequence of injury sustained on the battlefield as well as in civilian life. Hemorrhage accounts for nearly 50% of deaths on the battlefield and 39% of civilian trauma deaths. Monitoring the health status of combatants using easily obtained signals such as heart rate remains a challenge. This is especially true regarding remote monitoring and triage. Many confounding variables are possible when attempting to use heart rate as a vital sign. These include the affects of physical activity up to, during, and after injury. Currently, power spectral density (PSD) and fractal domain (FD) are being widely studied and advocated as a means to detect sensitive HRV changes due to HS. Unfortunately, traditional HRV analysis appears now to be unable to distinguish between central volume loss and exercise. This is problematic given the desire to use changes in heart rate to detect the presence of acute volume loss due to hemorrhage. In addition, little has been done to examine other physiologic signals for pattern changes indicative of critical changes in physiology in response to injury and treatment. Lastly, almost nothing has been done in the area of using the techniques of machine learning (ML) to enhance the predictive power of signal analyses as they relate to critical illness and injury and other clinical entities. What is lacking in the art therefore is a mathematical model for the evaluation of ECG signals using WT analysis. The model would allow for the extraction of features that can accurately identify various states of the cardiovascular system and distinguish between central volume blood loss and exercise.

SUMMARY OF THE INVENTION

[0022] The present invention relates to an apparatus for detecting and displaying a mammalian ECG waveform. The apparatus collects a plurality of sensor signals from at least two sensors in electronic communication with a sensor device worn on a body of the individual. The sensors are physiological sensors which utilize an output which is used to predict a heart-related parameter of the individual. The apparatus can help care workers estimate the extent of blood volume loss, distinguish blood volume loss from physiological activities associated with exercise and predict the presence an extent of cardiovascular disease. The apparatus has the ability to collect electronic heart-related signals from an individual and relate this data to an ECG waveform. In one embodiment, the apparatus is utilized to collected and analyze signals utilizing a mathematical operation to determine the presence of a heart-related injury or illness. [0023] Also disclosed is an apparatus that can help care workers detect and display a mammalian ECG waveform. The apparatus may be automated and is also adaptable or applicable to measuring a number of heart-related parameters and reporting the same and derivations of such parameters. The preferred embodiment, an apparatus to derive a ECG waveform, is directed to determining the heart-related health state of an individual. In other embodiments, the apparatus may allow for early identification of heart-related illness and early corrective action. [0024] In particular, the invention, according to one aspect, relates to an apparatus used in conjunction with a software platform for monitoring certain heart-related measures. These measures are then transformed into values of the measure of a ECG waveform, such as P, Q, R, S, and T components, using mathematical techniques which then have predictive value in regards to outcome in response to injury and illness.

[0025] An additional embodiment involves a method which utilizes an apparatus on the body that continuously monitors certain physiological parameters, such as heart-related electronic activity. Because the apparatus is continuously worn, the present method allows for the continuous collection of data during any physical activity performed by the user, including exercise activity and daily life activity. This data is then transformed into values of the measure of a ECG waveform, such as P, Q, R, S, and T components, using mathematical techniques which then have predictive value in regards to outcome in response to injury and illness.

[0026] The apparatus is further designed for comfort and convenience so that long term wear is not unreasonable within a wearer's lifestyle activities. It is to be specifically noted that the apparatus is designed for both continuous and long term wear. In one aspect, the apparatus is utilized by an individual before the onset of trauma so that baseline data may be collected. [0027] In an additional embodiment, the data collected by the apparatus is uploaded to the software platform for determining the existence of heart-related injury or illness. The measured data may be collected by the processor within the sensor device, a cell phone or other second device that wirelessly communicates, such as RF, IR, Bluetooth, WiFi, Wimax, RFiD. The collection may occur utilizing the sensor device and either this second device or in collaboration between the two devices, i.e., shared processing. These devices then determine the state, level of the criticality of the patient, etc. [0028] The system that is disclosed also provides an easy process for the entry and tracking of physical information . The user may choose from several methods of information input, such as direct, automatic, or manual input.

[0029] In an additional embodiment, an apparatus is disclosed for detecting and analyzing

ECG waveforms which includes at least two sensors adapted to be worn on an individual's body. The sensors utilized by the apparatus detect, in one aspect, heart-related electronic signals. The apparatus also includes a processor that receives at least a portion of the data indicative of heart- related parameters. The processor is adapted to generate derived data from at least a portion of the data. In one embodiment, the processor applies a mathematical operation which defines the heart- related electronic signals with an ECG waveform using wavelet transformation analysis. Such analysis identifies at least one time interval between a series of repeating wave components. The apparatus includes a display device so that the results of the analysis may be visualized. [0030] A method is disclosed for detecting and analyzing ECG waveforms which includes at least two sensors adapted to be worn on an individual's body. In one aspect, the sensors continuously detect heart-related electronic signals. A mathematical operation is applied to each set of heart related signals. This operation defines the heart-related electronic signals with an ECG waveform using wavelet transformation analysis. Such analysis identifies at least one time interval between a series of repeating wave components.

[0031] An additional method is disclosed for detecting and analyzing ECG waveforms which includes at least two sensors adapted to be worn on an individual's body. The sensors continuously detect heart-related electronic signals. A mathematical operation is applied to each set of heart related signals. This operation defines the heart-related electronic signals with an ECG waveform using wavelet transformation analysis. The derived ECG waveform is compared to a corresponding actual ECG waveform to confirm the accuracy of the mathematical operation. The derived waveform is then modified and then utilized to derive a subsequent ECG waveform, identifying at least one time interval between a series of repeating wave components. [0032] The apparatus may further include a housing adapted to be worn on the individual ' s body. The apparatus may further include a flexible body supporting the housing having first and second members that are adapted to wrap around a portion of the individual's body. The flexible body may support one or more of the sensors. The apparatus may further include wrapping means coupled to the housing for maintaining contact between the housing and the individual's body, and the wrapping means may support one or more of the sensors.

[0033] Another embodiment of the apparatus includes a central monitoring unit remote from the at least two sensors that includes a data storage device. The data storage device receives the derived data from the processor and retrievably stores the derived data therein. The apparatus also includes means for transmitting information based on the derived data from the central monitoring unit to a recipient, which recipient may include the individual or a third party authorized by the individual. The processor may be supported by a housing adapted to be worn on the individual's body, or alternatively may be part of the central monitoring unit.

[0034] In one embodiment of the apparatus, the processor and the memory are included in a wearable sensor device. In another embodiment, the apparatus includes a wearable sensor device, the processor and the memory being included in a computing device located separately from the sensor device, wherein the sensor signals are transmitted from the sensor device to the computing device.

[0035] The present invention relates to an apparatus for detecting and analyzing a mammalian ECG waveform. The apparatus collects a plurality of sensor signals from at least two sensors in electronic communication with a sensor device worn on a body of the individual. The sensors are physiological sensors which utilize an output which is used to predict a heart-related parameter of the individual. The apparatus also includes a processor that receives at least a portion of the data indicative of heart-related parameters. In one embodiment, the processor applies a mathematical operation which defines the association of the heart-related signals with an ECG waveform that has repeating wave units. Such analysis is done using wavelet transformation analysis. Such analysis identifies at least one time interval between a series of repeating wave components. The apparatus includes a display device so that the results of the analysis may be visualized.

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Further features and advantages of the present invention will be apparent upon consideration of the following detailed description of the present invention, taken in conjunction with the following drawings, in which like reference characters refer to like parts, and in which: [0037] Fig. 1 is a diagram of an embodiment of a system for monitoring physiological data and lifestyle over an electronic network according to the present invention; [0038] Fig. 2 is a block diagram of an embodiment of the sensor device shown in Fig. 1;

[0039] Fig. 3 is a block diagram of an embodiment of the central monitoring unit shown in

Fig. 1;

[0040] Fig. 4 is a block diagram of an alternate embodiment of the central monitoring unit shown in Fig. 1;

[0041] Fig. 5 is a front view of a specific embodiment of the sensor device shown in Fig. 1 ;

[0042] Fig. 6 is a back view of a specific embodiment of the sensor device shown in Fig. 1 ;

[0043] Fig. 7 is a side view of a specific embodiment of the sensor device shown in Fig. 1 ;

[0044] Fig. 8 is a bottom view of a specific embodiment of the sensor device shown in Fig. l;

[0045] Figs. 9 and 10 are front perspective views of a specific embodiment of the sensor device shown in Fig. 1;

[0046] Fig. 11 is an exploded side perspective view of a specific embodiment of the sensor device shown in Fig. 1;

[0047] Fig. 12 is a side view of the sensor device shown in Figs. 5 through 11 inserted into a battery recharger unit; [0048] Fig. 13 is a block diagram illustrating all of the components either mounted on or coupled to the printed circuit board forming a part of the sensor device shown in Figs. 5 through 11 ;

[0049] Fig. 14 is a block diagram showing the format of algorithms that are developed according to an aspect of the present invention;

[0050] Fig. 15 is a block diagram illustrating an example algorithm for predicting energy expenditure according to the present invention;

[0051] Fig. 16A is a front view of a specific embodiment of the sensor device;

[0052] Fig. 16B is an illustration of the device of 16A when worn on the arm of a subject;

[0053] Figs. 17 A and 17B are a comparison of metabolic cart EE and predicted EE in a level

1 trauma patient in a bedside situation;

[0054] Figs. 18A and 18B are a comparison of shock index and predicted EE in a level 1 trauma bedside situation; and

[0055] Figs. 19A, 19B and 19C are back, front and back views, respectively, of the left arm showing electrode placement locations according to an aspect of the present invention;

[0056] Figs. 2OA and 2OB are back and front views, respectively, of the right arm showing electrode placement locations according to an aspect of the present invention;

[0057] Figs. 2OC, 2OD and 2OE are front, back and front views, respectively of the torso showing electrode placement locations according to an aspect of the present invention;

[0058] Fig. 21 is a block diagram of a circuit for detecting an ECG signal from according to an embodiment of the present invention;

[0059] Figs. 22A and 22B are circuit diagrams of first and second embodiments of the bias/coupling network shown in Figures 21 and 24;

[0060] Fig. 22C is a circuit diagram of a first stage amplifier design;

[0061] Fig. 23 is a circuit diagram of one embodiment of the filter shown in Figures 4 and 7;

[0062] Fig. 24 is a block diagram of a circuit for detecting an ECG signal from according to an alternate embodiment of the present invention; [0063] Figs. 24A through 24D are diagrammatic representations of detected ECG signals through various stages of processing;

[0064] Figs. 24E through 24H are diagrammatic representations of detected ECG signals through various stages of beat detection;

[0065] Figs. 25A through 25F are block diagrams of alternative circuits for detecting an

ECG signal from according to an alternate embodiment of the present invention;

[0066] Fig. 26 is a diagram of a typical peak forming a part of the signal generated according to the present invention;

[0067] Figs. 26 and 27A and 27B are diagrams of a typical up-down-up sequence forming a part of the signal generated according to the present invention;

[0068] Fig. 28 is a graph illustrating measured ECG signal as a function of time

[0069] Fig. 29 is a bottom plan view of one embodiment of the armband body monitoring device;

[0070] Fig. 30 is a bottom plan view of a second embodiment of the armband body monitoring device;

[0071] Fig. 31 is a bottom plan view of a third embodiment of the armband body monitoring device;

[0072] Fig. 32 is a bottom plan view of a fourth embodiment of the armband body monitoring device;

[0073] Fig. 33 is a bottom plan view of a fifth embodiment of the armband body monitoring device;

[0074] Fig. 34 is a bottom plan view of a sixth embodiment of the armband body monitoring device;

[0075] Fig. 53 is a bottom plan view of a seventh embodiment of the armband body monitoring device; [0076] Fig. 36 is an isometric view of the seventh embodiment of the armband body monitoring device mounted upon a human arm;

[0077] Fig. 37 is an isometric view of an eighth embodiment of the armband body monitoring device;

[0078] Fig. 38A is a top plan view of a ninth embodiment of the armband body monitoring device;

[0079] Fig. 38B is a bottom plan view of a ninth embodiment of the armband body monitoring device;

[0080] Fig. 38C is a sectional view of the embodiment of Figure 38B taken along line A-A;

[0081] Fig. 39 is a diagram of ECG one cycle trace based upon cardiac physiology;

[0082] Fig. 40 is a schematic diagram for ECG analysis;

[0083] Fig.41 is a diagram illustrating that an amplitude response of the digital bandpass (3 dB) is 1-55 Hz;

[0084] Fig. 42a is a diagram illustrating a 17 second ECG signal before baseline drift removal;

[0085] Fig. 42b is a diagram illustrating a 17 second after baseline drift removal;

[0086] Fig. 43 is a diagram illustrating the decomposition of a signal with filter bank cascading LPF and HPF;

[0087] Fig. 44 is a diagram of an ECG signal after wavelet transformation with the approximation and the detailed coefficient using Haar at level;

[0088] Fig. 45 is a diagram illustrating a detailed coefficient using Haar at level 4 squared and threshold at 1.5 standard deviation;

[0089] Fig. 46 is a diagram of the detailed procedure of main QRS detection methods;

[0090] Fig. 47 is an example of LBNP signal;

[0091] Fig. 48 is a diagram showing a normal ECG;

[0092] Fig. 49 is a filter function block diagram of the ECG signal; [0093] Fig. 50 is an example of 60 Hz power-line interfering noise before (upper) and after

(bottom) filtering;

[0094] Fig. 51 is a detailed schematic diagram of QRS wave detection process;

[0095] Fig. 52 is a diagram of an example of QRS detection steps;

[0096] Fig. 53 is a detailed schematic diagram of feature extraction;

[0097] Fig. 54 is a detailed schematic diagram of DWT level 2;

[0098] Figs. 55a - 55c are diagrams of pattern features for LBNP and exercise subjects;

[0099] Figs. 56a and 56b illustrate the average pattern and standard deviation of LBNP and exercise groups at different stages using traditional HRV measuring methods;

[00100] Fig. 57 is a schematic diagram of the classification process; and

[00101] Fig. 58 is a graph illustrating the testing ability of the wavelet feature.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[00102] In general, the device and method of the present invention utilizes development of mathematic formulas and/or algorithms to determine heart rate variability, vital sign and physiological analysis.

[00103] In one aspect of the present invention, data relating to the physiological state and certain contextual parameters of an individual are collected and transmitted, either subsequently or in real-time, to a site, preferably remote from the individual, where it is stored for later manipulation and presentation to a recipient, preferably over an electronic network such as the Internet. It is to be understood that the term "individual" may refer to any mammal. Referring to Fig.1 , located at user location 5 is sensor device 10 adapted to be placed in proximity with at least a portion of a mammalian body. Sensor device 10 may be placed on a portion of any mammalian body. Sensor device 10 is preferably worn by an individual user on his or her body, for example as part of a garment such as a form fitting shirt, or as part of an arm band or the like. Sensor device 10, includes one or more sensors, which are adapted to generate signals in response to physiological characteristics of an individual, and a microprocessor. Proximity as used herein means that the sensors of sensor device 10 are separated from the individual's body by a material or the like, or a distance such that the capabilities of the sensors are not impeded. While in other embodiments, Sensor Device 10 is meant to comprise a device having all sensing, and optionally, processing capabilities therein, other embodiments allow for the sensing capabilities and processing capabilities to be spread across separate devices having partial or complete capabilities as those described herein for the Sensor Device 10 in electronic communication with one another Sensor device 10 generates data indicative of various physiological parameters of an individual, such as the individual's heart rate, pulse rate, beat-to-beat heart variability, EKG or ECG, body impedance, respiration rate, skin temperature, core body temperature, heat flow off the body, galvanic skin response or GSR, EMG, EEG, EOG, blood pressure, body fat, hydration level, activity level, oxygen consumption, glucose or blood sugar level, body position, pressure on muscles or bones, and UV radiation exposure and absorption. In certain cases, the data indicative of the various physiological parameters is the signal or signals themselves generated by the one or more sensors and in certain other cases the data is calculated by the microprocessor based on the signal or signals generated by the one or more sensors. Methods for generating data indicative of various physiological parameters and sensors to be used therefor are well known. Table 1 provides several examples of such well known methods and shows the parameter in question, an example method used, an example sensor device used, and the signal that is generated. Table 1 also provides an indication as to whether further processing based on the generated signal is required to generate the data. Table 1

Figure imgf000021_0001
Figure imgf000022_0001

[00104] It is to be specifically noted that a number of other types and categories of sensors may be utilized alone or in conjunction with those given above, including but not limited to relative and global positioning sensors for determination of location of the user; torque & rotational acceleration for determination of orientation in space; blood chemistry sensors; interstitial fluid chemistry sensors; bio-impedance sensors; invasive lactate sensors, and several contextual sensors, such as: pollen, humidity, ozone, acoustic, body and ambient noise and sensors adapted to utilize the device in a biofϊngerprinting scheme.

[00105] The types of data listed in Table 1 are intended to be examples of the types of data that can be generated by sensor device 10. It is to be understood that other types of data relating to other parameters can be generated by sensor device 10 without departing from the scope of the present invention.

[00106] The microprocessor of sensor device 10 may be programmed to summarize and analyze the data. For example, the microprocessor can be programmed to calculate an average, minimum or maximum heart rate or respiration rate over a defined period of time, such as ten minutes. Sensor device 10 may be able to derive information relating to a mammal ' s physiological state based on the data indicative of one or more physiological parameters. Yet, it should be understood that the microprocessor is programmed to do much more. For example, the microprocessor of sensor device 10 is programmed to derive such information using known methods based on the data indicative of one or more physiological parameters. Table 2 provides a non- exhaustive list of the type of information that can be derived, and indicates some of the types of data that can be used as inputs for the derivation. The methods and techniques disclosed herein and particularly in U.S. Patent Application Serial No. 10/682,293 enable each of the parameters below (among others) to be derived any combination of inputs signals disclosed below or herein. Thus, it should be understood that any sensed parameter disclosed herein, i.e., input signal to a derivation, can be used alone or in combination with any other to derive the derived parameters listed herein. Table 2

Figure imgf000024_0001

[00107] Additionally, sensor device 10 may also generate data indicative of various contexual parameters relating to the individual. Deriving a "context" (and any roots or derivations of the term used herein) means generating data about the circumstance, condition, environment, or setting of an individual. As a non limiting example, sensor device 10 can generate data indicative of the air quality, sound level/quality, light quality or ambient temperature near the individual, the global positioning of the individual, whether someone is driving in a car, lying down, running or standing up. Some contextual derivations can also be properly classified as activities and will be apparent to skilled artisan when such is the case. Sensor device 10 may include one or more sensors for generating signals in response to contextual characteristics relating to the environment surrounding the individual, the signals ultimately being used to generate the type of data described above. Such sensors are well known, as are methods for generating contextual parametric data such as air quality, sound level/quality, ambient temperature and global positioning.

[00108] Fig. 2 is a block diagram of an embodiment of sensor device 10. Sensor device 10 includes at least one sensor 12 and microprocessor 20. Depending upon the nature of the signal generated by sensor 12, the signal can be sent through one or more of amplifier 14, conditioning circuit 16, and analog-to-digital converter 18, before being sent to microprocessor 20. For example, where sensor 12 generates an analog signal in need of amplification and filtering, that signal can be sent to amplifier 14, and then on to conditioning circuit 16, which may, for example, be a band pass filter. The amplified and conditioned analog signal can then be transferred to analog-to-digital converter 18, where it is converted to a digital signal. The digital signal is then sent to microprocessor 20. Alternatively, if sensor 12 generates a digital signal, that signal can be sent directly to microprocessor 20.

[00109] A digital signal or signals representing certain physiological and/or contextual characteristics of the individual user may be used by microprocessor 20 to calculate or generate data indicative of physiological and/or contextual parameters of the individual user. Microprocessor 20 is programmed to derive information relating to at least one aspect of the individual's physiological state . It should be understood that microprocessor 20 may also comprise other forms of processors or processing devices, such as a microcontroller, or any other device that can be programmed to perform the functionality described herein. [00110] Optionally, central processing unit may provide operational control or, at a minimum, selection of an audio player device 21. As will be apparent to those skilled in the art, audio player 21 is of the type which either stores and plays or plays separately stored audio media. The device may control the output of audio player 21 , as described in more detail below, or may merely furnish a user interface to permit control of audio player 21 by the wearer. [00111] The data indicative of physiological and/or contextual parameters can, according to one embodiment of the present invention, be sent to memory 22, such as flash memory, where it is stored until uploaded in the manner to be described below. Although memory 22 is shown in Fig. 2 as a discrete element, it will be appreciated that it may also be part of microprocessor 20. Sensor device 10 also includes input/output circuitry 24, which is adapted to output and receive as input certain data signals in the manners to be described herein. Thus, memory 22 of the sensor device 10 will build up, over time, a store of data relating to the individual user's body and/or environment. That data is periodically uploaded from sensor device 10 and sent to remote central monitoring unit 30, as shown in Fig. 1 , where it is stored in a database for subsequent processing and presentation to the user, preferably through a local or global electronic network such as the Internet. This uploading of data can be an automatic process that is initiated by sensor device 10 periodically or upon the happening of an event such as the detection by sensor device 10 of a heart rate below a certain level, or can be initiated by the individual user or some third party authorized by the user, preferably according to some periodic schedule, such as every day at 10:00 p.m. Alternatively, rather than storing data in memory 22, sensor device 10 may continuously upload data in real time. [00112] The uploading of data from sensor device 10 to central monitoring unit 30 for storage can be accomplished in various ways. In one embodiment, the data collected by sensor device 10 is uploaded by first transferring the data to personal computer 35 shown in Fig. 1 by means of physical connection 40, which, for example, may be a serial connection such as an RS232 or USB port. This physical connection may also be accomplished by using a cradle, not shown, that is electronically coupled to personal computer 35 into which sensor device 10 can be inserted, as is common with many commercially available personal digital assistants. The uploading of data could be initiated by then pressing a button on the cradle or could be initiated automatically upon insertion of sensor device 10 or upon proximity to a wireless receiver. The data collected by sensor device 10 may be uploaded by first transferring the data to personal computer 35 by means of short-range wireless transmission, such as infrared or RF transmission, as indicated at 45.

[00113] Once the data is received by personal computer 35, it is optionally compressed and encrypted by any one of a variety of well known methods and then sent out over a local or global electronic network, preferably the Internet, to central monitoring unit 30. It should be noted that personal computer 35 can be replaced by any computing device that has access to and that can transmit and receive data through the electronic network, such as, for example, a personal digital assistant such as the Palm VII sold by Palm, Inc., or the Blackberry 2- way pager sold by Research in Motion, Inc.

[00114] Alternatively, the data collected by sensor device 10, after being encrypted and, optionally, compressed by microprocessor 20, may be transferred to wireless device 50, such as a 2-way pager or cellular phone, for subsequent long distance wireless transmission to local telco site 55 using a wireless protocol such as e-mail or as ASCII or binary data. Local telco site 55 includes tower 60 that receives the wireless transmission from wireless device 50 and computer 65 connected to tower 60. According to the preferred embodiment, computer 65 has access to the relevant electronic network, such as the Internet, and is used to transmit the data received in the form of the wireless transmission to the central monitoring unit 30 over the Internet. Although wireless device 50 is shown in Fig. 1 as a discrete device coupled to sensor device 10, it or a device having the same or similar functionality may be embedded as part of sensor device 10.

[00115] Sensor device 10 may be provided with a button to be used to time stamp events such as time to bed, wake time, and time of meals. These time stamps are stored in sensor device 10 and are uploaded to central monitoring unit 30 with the rest of the data as described above. The time stamps may include a digitally recorded voice message that, after being uploaded to central monitoring unit 30, are translated using voice recognition technology into text or some other information format that can be used by central monitoring unit 30. Note that in an alternate embodiment, these time-stamped events can be automatically detected.

[00116] In addition to using sensor device 10 to automatically collect physiological data relating to an individual user, a kiosk could be adapted to collect such data by, for example, weighing the individual, providing a sensing device similar to sensor device 10 on which an individual places his or her hand or another part of his or her body, or by scanning the individual's body using, for example, laser technology or an iStat blood analyzer. The kiosk would be provided with processing capability as described herein and access to the relevant electronic network, and would thus be adapted to send the collected data to the central monitoring unit 30 through the electronic network. A desktop sensing device, again similar to sensor device 10, on which an individual places his or her hand or another part of his or her body may also be provided. For example, such a desktop sensing device could be a lactate monitor in which an individual places his or her arm. An individual might also wear a ring having a sensor device 10 incorporated therein. A base, not shown, could then be provided which is adapted to be coupled to the ring. The desktop sensing device or the base just described may then be coupled to a computer such as personal computer 35 by means of a physical or short range wireless connection so that the collected data could be uploaded to central monitoring unit 30 over the relative electronic network in the manner described above. A mobile device such as, for example, a personal digital assistant, might also be provided with a sensor device 10 incorporated therein. Such a sensor device 10 would be adapted to collect data when mobile device is placed in proximity with the individual's body, such as by holding the device in the palm of one's hand, and upload the collected data to central monitoring unit 30 in any of the ways described herein.

[00117] An alternative embodiment includes the incorporation of third party devices, not necessary worn on the body, collect additional data pertaining to physiological conditions. Examples include portable blood analyzers, glucose monitors, weight scales, blood pressure cuffs, pulse oximeters, CPAP machines, portable oxygen machines, home thermostats, treadmills, cell phones and GPS locators. The system could collect from, or in the case of a treadmill or CPAP, control these devices, and collect data to be integrated into the streams for real time or future derivations of new parameters. An example of this is a pulse oximeter on the user's finger could help measure pulse and therefore serve a surrogate reading for blood pressure. Additionally, a user could utilize one of these other devices to establish baseline readings in order to calibrate the device. [00118] Furthermore, in addition to collecting data by automatically sensing such data in the manners described above, individuals can also manually provide data relating to various parameters that is ultimately transferred to and stored at central monitoring unit 30. An individual user can access a web site maintained by central monitoring unit 30 and can directly input information relating to physiological conditions by entering text freely, by responding to questions posed by the web site, or by clicking through dialog boxes provided by the web site. Central monitoring unit 30 can also be adapted to periodically send electronic mail messages containing questions designed to elicit information relating to life activities to personal computer 35 or to some other device that can receive electronic mail, such as a personal digital assistant, a pager, or a cellular phone. The individual would then provide data relating to life activities to central monitoring unit 30 by responding to the appropriate electronic mail message with the relevant data. Central monitoring unit 30 may also be adapted to place a telephone call to an individual user in which certain questions would be posed to the individual user. The user could respond to the questions by entering information using a telephone keypad, or by voice, in which case conventional voice recognition technology would be used by central monitoring unit 30 to receive and process the response. The telephone call may also be initiated by the user, in which case the user could speak to a person directly or enter information using the keypad or by voice/voice recognition technology. Central monitoring unit 30 may also be given access to a source of information controlled by the user, for example the user's electronic calendar such as that provided with the Outlook product sold by Microsoft Corporation of Redmond, Washington, from which it could automatically collect information.

[00119] Feedback may also be provided to a user directly through sensor device 10 in a visual form, for example through an LED or LCD or by constructing sensor device 10, at least in part, of a thermochromatic plastic, in the form of an acoustic signal or in the form of tactile feedback such as vibration. Additionally, a reminder or alert can be issued in the event that a particular physiological parameter has been detected, such as high lactate levels have been encountered. [00120] As will be apparent to those of skill in the art, it may be possible to download data from central monitoring unit 30 to sensor device 10. The flow of data in such a download process would be substantially the reverse of that described above with respect to the upload of data from sensor device 10. Thus, it is possible that the firmware of microprocessor 20 of sensor device 10 can be updated or altered remotely, i.e., the microprocessor can be reprogrammed, by downloading new firmware to sensor device 10 from central monitoring unit 30 for such parameters as timing and sample rates of sensor device 10. Also, the reminders/alerts provided by sensor device 10 may be set by the user using the web site maintained by central monitoring unit 30 and subsequently downloaded to the sensor device 10.

[00121] Referring to Fig. 3, a block diagram of an embodiment of central monitoring unit 30 is shown. Central monitoring unit 30 includes CSU/DSU 70 which is connected to router 75, the main function of which is to take data requests or traffic, both incoming and outgoing, and direct such requests and traffic for processing or viewing on the web site maintained by central monitoring unit 30. Connected to router 75 is firewall 80. The main purpose of firewall 80 is to protect the remainder of central monitoring unit 30 from unauthorized or malicious intrusions. Switch 85, connected to firewall 80, is used to direct data flow between middleware servers 95a through 95c and database server 110. Load balancer 90 is provided to spread the workload of incoming requests among the identically configured middleware servers 95a through 95c. Load balancer 90, a suitable example of which is the F5 Serverlron product sold by Foundry Networks, Inc. of San Jose, California, analyzes the availability of each middleware server 95a through 95c, and the amount of system resources being used in each middleware server 95 a through 95 c, in order to spread tasks among them appropriately.

[00122] Central monitoring unit 30 includes network storage device 100, such as a storage area network or SAN, which acts as the central repository for data. In particular, network storage device 100 comprises a database that stores all data gathered for each individual user in the manners described above. An example of a suitable network storage device 100 is the Symmetrix product sold by EMC Corporation of Hopkinton, Massachusetts. Although only one network storage device 100 is shown in Fig. 3, it will be understood that multiple network storage devices of various capacities could be used depending on the data storage needs of central monitoring unit 30. Central monitoring unit 30 also includes database server 110 which is coupled to network storage device 100. Database server 110 is made up of two main components: a large scale multiprocessor server and an enterprise type software server component such as the 8/8i component sold by Oracle Corporation of Redwood City, California, or the 506 7 component sold by Microsoft Corporation of Redmond, Washington. The primary functions of database server 110 are that of providing access upon request to the data stored in network storage device 100, and populating network storage device 100 with new data. Coupled to network storage device 100 is controller 115, which typically comprises a desktop personal computer, for managing the data stored in network storage device 100. [00123] Middleware servers 95a through 95c, a suitable example of which is the 22OR Dual

Processor sold by Sun Microsystems, Inc. of Palo Alto, California, each contain software for generating and maintaining the corporate or home web page or pages of the web site maintained by central monitoring unit 30. As is known in the art, a web page refers to a block or blocks of data available on the World-Wide Web comprising a file or files written in Hypertext Markup Language or HTML, and a web site commonly refers to any computer on the Internet running a World-Wide Web server process. The corporate or home web page or pages are the opening or landing web page or pages that are accessible by all members of the general public that visit the site by using the appropriate uniform resource locator or URL. As is known in the art, URLs are the form of address used on the World-Wide Web and provide a standard way of specifying the location of an object, typically a web page, on the Internet. Middleware servers 95a through 95c also each contain software for generating and maintaining the web pages of the web site of central monitoring unit 30 that can only be accessed by individuals that register and become members of central monitoring unit 30. The member users will be those individuals who wish to have their data stored at central monitoring unit 30. Access by such member users is controlled using passwords for security purposes. Preferred embodiments of those web pages are described in detail below and are generated using collected data that is stored in the database of network storage device 100. [00124] Middleware servers 95a through 95c also contain software for requesting data from and writing data to network storage device 100 through database server 110. When an individual user desires to initiate a session with the central monitoring unit 30 for the purpose of entering data into the database of network storage device 100, viewing his or her data stored in the database of network storage device 100, or both, the user visits the home web page of central monitoring unit 30 using a browser program such as Internet Explorer distributed by Microsoft Corporation of Redmond, Washington, and logs in as a registered user. Load balancer 90 assigns the user to one of the middleware servers 95a through 95c, identified as the chosen middleware server. A user will preferably be assigned to a chosen middleware server for each entire session. The chosen middleware server authenticates the user using any one of many well known methods, to ensure that only the true user is permitted to access the information in the database. A member user may also grant access to his or her data to a third party such as a health care provider or a personal trainer. Each authorized third party may be given a separate password and may view the member user' s data using a conventional browser. It is therefore possible for both the user and the third party to be the recipient of the data.

[00125] When the user is authenticated, the chosen middleware server requests, through database server 110, the individual user's data from network storage device 100 fora predetermined time period. The predetermined time period is preferably thirty days. The requested data, once received from network storage device 100, is temporarily stored by the chosen middleware server in cache memory. The cached data is used by the chosen middleware server as the basis for presenting information, in the form of web pages, to the user again through the user's browser. Each middleware server 95a through 95c is provided with appropriate software for generating such web pages, including software for manipulating and performing calculations utilizing the data to put the data in appropriate format for presentation to the user. Once the user ends his or her session, the data is discarded from cache. When the user initiates a new session, the process for obtaining and caching data for that user as described above is repeated. This caching system thus ideally requires that only one call to the network storage device 100 be made per session, thereby reducing the traffic that database server 110 must handle. Should a request from a user during a particular session require data that is outside of a predetermined time period of cached data already retrieved, a separate call to network storage device 100 may be performed by the chosen middleware server. The predetermined time period should be chosen, however, such that such additional calls are minimized. Cached data may also be saved in cache memory so that it can be reused when a user starts a new session, thus eliminating the need to initiate a new call to network storage device 100. [00126] As described in connection with Table 2, the microprocessor of sensor device 10 may be programmed to derive information relating to an individual's physiological state based on the data indicative of one or more physiological parameters. Central monitoring unit 30, and preferably middleware servers 95 a through 95 c, may also be similarly programmed to derive such information based on the data indicative of one or more physiological parameters.

[00127] It is also contemplated that a user will input additional data during a session, for example, information relating to the user's eating or sleeping habits. This additional data is preferably stored by the chosen middleware server in a cache during the duration of the user's session. When the user ends the session, this additional new data stored in a cache is transferred by the chosen middleware server to database server 110 for population in network storage device 100. Alternatively, in addition to being stored in a cache for potential use during a session, the input data may also be immediately transferred to database server 110 for population in network storage device 100, as part of a write-through cache system which is well known in the art. [00128] Data collected by sensor device 10 shown in Fig. 1 is periodically uploaded to central monitoring unit 30. Either by long distance wireless transmission or through personal computer 35, a connection to central monitoring unit 30 is made through an electronic network, preferably the Internet. In particular, connection is made to load balancer 90 through CSU/DSU 70, router 75, firewall 80 and switch 85. Load balancer 90 then chooses one of the middleware servers 95a through 95c to handle the upload of data, hereafter called the chosen middleware server. The chosen middleware server authenticates the user using any one of many well known methods. If authentication is successful, the data is uploaded to the chosen middleware server as described above, and is ultimately transferred to database server 110 for population in the network storage device 100.

[00129] Referring to Fig. 4, an alternate embodiment of central monitoring unit 30 is shown.

In addition to the elements shown and described with respect to Fig. 3, the embodiment of the central monitoring unit 30 shown in Fig. 4 includes a mirror network storage device 120 which is a redundant backup of network storage device 100. Coupled to mirror network storage device 120 is controller 122. Data from network storage device 100 is periodically copied to mirror network storage device 120 for data redundancy purposes.

[00130] Third parties such as insurance companies or research institutions may be given access, possibly for a fee, to certain of the information stored in mirror network storage device 120. Preferably, in order to maintain the confidentiality of the individual users who supply data to central monitoring unit 30, these third parties are not given access to such user's individual database records, but rather are only given access to the data stored in mirror network storage device 120 in aggregate form. Such third parties may be able to access the information stored in mirror network storage device 120 through the Internet using a conventional browser program. Requests from third parties may come in through CSU/DSU 70, router 75 , firewall 80 and switch 85. In the embodiment shown in Fig. 4, a separate load balancer 130 is provided for spreading tasks relating to the accessing and presentation of data from mirror drive array 120 among identically configured middleware servers 135a through 135c. Middleware servers 135a through 135c each contain software for enabling the third parties to, using a browser, formulate queries for information from mirror network storage device 120 through separate database server 125. Middleware servers 135a through 135c also contain software for presenting the information obtained from mirror network storage device 120 to the third parties over the Internet in the form of web pages. In addition, the third parties can choose from a series of prepared reports that have information packaged along subject matter lines, such as various demographic categories.

[00131] As will be apparent to one of skill in the art, instead of giving these third parties access to the backup data stored in mirror network storage device 120, the third parties may be given access to the data stored in network storage device 100. Also, instead of providing load balancer 130 and middleware servers 135a through 135c, the same functionality, although at a sacrificed level of performance, could be provided by load balancer 90 and middleware servers 95a through 95c.

[00132] The Manager web pages comprise a utility through which central monitoring unit 30 provides various types and forms of data, commonly referred to as analytical status data, to the user that is generated from the data it collects or generates, namely one or more of: the data indicative of various physiological parameters generated by sensor device 10; the data derived from the data indicative of various physiological parameters; the data indicative of various contextual parameters generated by sensor device 10; and the data input by the user. Analytical status data is characterized by the application of certain utilities or algorithms to convert one or more of the data indicative of various physiological parameters generated by sensor device 10, the data derived from the data indicative of various physiological parameters, the data indicative of various contextual parameters generated by sensor device 10, and the data input by the user into calculated health, wellness and lifestyle indicators. As another example, skin temperature, heart rate, respiration rate, heat flow and/or GSR can be used to provide an indicator to the user of his or her stress level over a desired time period. As still another example, skin temperature, heat flow, beat-to-beat heart variability, heart rate, pulse rate, respiration rate, core temperature, galvanic skin response, EMG, EEG, EOG, blood pressure, oxygen consumption, ambient sound and body movement or motion as detected by a device such as an accelerometer can be used to provide indicators to the user of his or her sleep patterns over a desired time period.

[00133] In a variety of the embodiments described above, itis specifically contemplated that the data be input or detected by the system for derivation of the necessary data. One aspect of the present invention relates to a sophisticated algorithm development process for creating a wide range of algorithms for generating information relating to a variety of variables from the data received from the plurality of physiological and/or contextual sensors on sensor device 400. Such variables may include, without limitation, VO2 levels, energy expenditure, including resting, active and total values, daily caloric intake, sleep states, including in bed, sleep onset, sleep interruptions, wake, and out of bed, and activity states, including exercising, sitting, traveling in a motor vehicle, and lying down, and the algorithms for generating values for such variables may be based on data from, for example, the 2-axis accelerometer, the heat flux sensor, the GSR sensor, the skin temperature sensor, the near-body ambient temperature sensor, and the heart rate sensor in the embodiment described above.

[00134] Note that there are several types of algorithms that can be computed. For example, and without limitation, these include algorithms for predicting user characteristics, continual measurements, durative contexts, instantaneous events, and cumulative conditions. User characteristics include permanent and semi-permanent parameters of the wearer, including aspects such as weight, height, and wearer identity. An example of a continual measurement is energy expenditure, which constantly measures, for example on a minute by minute basis, the number of calories of energy expended by the wearer. Durative contexts are behaviors that last some period of time, such as sleeping, driving a car, or jogging. Instantaneous events are those that occur at a fixed or over a very short time period, such as a heart attack or falling down. Cumulative conditions are those where the person's condition can be deduced from their behavior over some previous period of time. For example, if a person hasn't slept in 36 hours and hasn't eaten in 10 hours, it is likely that they are fatigued. Table 3 below shows numerous examples of specific personal characteristics, continual measurements, durative measurements, instantaneous events, and cumulative conditions.

TABLE 3

Figure imgf000037_0001
Figure imgf000038_0001

[00135] It will be appreciated that the present invention may be utilized in a method for doing automatic journaling of a wearer's physiological and contextual states. The system can automatically produce a journal of what activities the user was engaged in, what events occurred, how the user's physiological state changed over time, and when the user experienced or was likely to experience certain conditions. For example, the system can produce a record of when the user exercised, drove a car, slept, was in danger of heat stress, or ate, in addition to recording the user's hydration level, energy expenditure level, sleep levels, and alertness levels throughout a day. [00136] According to the algorithm development process, linear or non-linear mathematical models or algorithms are constructed that map the data from the plurality of sensors to a desired variable. The process consists of several steps. First, data is collected by subjects wearing, for example, sensor device 400 who are put into situations as close to real world situations as possible, with respect to the parameters being measured, such that the subjects are not endangered and so that the variable that the proposed algorithm is to predict can, at the same time, be reliably measured using, for example, highly accurate medical grade lab equipment. This first step provides the following two sets of data that are then used as inputs to the algorithm development process: (i) the raw data from sensor device 400, and (ii) the data consisting of the verifiably accurate data measurements and extrapolated or derived data made with or calculated from the more accurate lab equipment, such as a VO2 measurement device or indirect calorimeter. This verifiable data becomes a standard against which other analytical or measured data is compared. For cases in which the variable that the proposed algorithm is to predict relates to context detection, such as traveling in a motor vehicle, the verifiable standard data is provided by the subjects themselves, such as through information input manually into sensor device 400, a PC, or otherwise manually recorded. The collected data, i.e., both the raw data and the corresponding verifiable standard data, is then organized into a database and is split into training and test sets.

[00137] Next, using the data in the training set, a mathematical model is built that relates the raw data to the corresponding verifiable standard data. Specifically, a variety of machine learning techniques are used to generate two types of algorithms: 1) algorithms known as features, which are derived continuous parameters that vary in a manner that allows the prediction of the lab-measured parameter for some subset of the data points. The features are typically not conditionally independent of the lab-measured parameter e.g., VO2 level information from a metabolic cart, douglas bag, or doubly labeled water, and 2) algorithms known as context detectors that predict various contexts, e.g., running, exercising, lying down, sleeping or driving, useful for the overall algorithm. A number of well known machine learning techniques may be used in this step, including artificial neural nets, decision trees, memory-based methods, boosting, attribute selection through cross-validation, and stochastic search methods such as simulated annealing and evolutionary computation.

[00138] After a suitable set of features and context detectors are found, several well known machine learning methods are used to combine the features and context detectors into an overall model. Techniques used in this phase include, but are not limited to, multilinear regression, locally weighted regression, decision trees, artificial neural networks, stochastic search methods, support vector machines, and model trees. These models are evaluated using cross-validation to avoid over- fitting.

[00139] At this stage, the models make predictions on, for example, a minute by minute basis.

Inter-minute effects are next taken into account by creating an overall model that integrates the minute by minute predictions. A well known or custom windowing and threshold optimization tool may be used in this step to take advantage of the temporal continuity of the data. Finally, the model's performance can be evaluated on the test set, which has not yet been used in the creation of the algorithm. Performance of the model on the test set is thus a good estimate of the algorithm's expected performance on other unseen data. Finally, the algorithm may undergo live testing on new data for further validation.

[00140] Further examples of the types of non-linear functions and/or machine learning method that may be used in the present invention include the following: conditionals, case statements, logical processing, probabilistic or logical inference, neural network processing, kernel based methods, memory-based lookup including kNN and SOMs, decision lists, decision-tree prediction, support vector machine prediction, clustering, boosted methods, cascade-correlation, Boltzmann classifiers, regression trees, case-based reasoning, Gaussians, Bayes nets, dynamic Bayesian networks, HMMs, Kalman filters, Gaussian processes and algorithmic predictors, e.g. learned by evolutionary computation or other program synthesis tools.

[00141] Although one can view an algorithm as taking raw sensor values or signals as input, performing computation, and then producing a desired output, it is useful in one preferred embodiment to view the algorithm as a series of derivations that are applied to the raw sensor values. Each derivation produces a signal referred to as a derived channel. The raw sensor values or signals are also referred to as channels, specifically raw channels rather than derived channels. These derivations, also referred to as functions, can be simple or complex but are applied in a predetermined order on the raw values and, possibly, on already existing derived channels. The first derivation must, of course, only take as input raw sensor signals and other available baseline information such as manually entered data and demographic information about the subject, but subsequent derivations can take as input previously derived channels. Note that one can easily determine, from the order of application of derivations, the particular channels utilized to derive a given derived channel. Also note that inputs that a user provides on an Input/Output, or I/O, device or in some fashion can also be included as raw signals which can be used by the algorithms. In one embodiment, the raw signals are first summarized into channels that are sufficient for later derivations and can be efficiently stored. These channels include derivations such as summation, summation of differences, and averages. Note that although summarizing the high-rate data into compressed channels is useful both for compression and for storing useful features, it may be useful to store some or all segments of high rate data as well, depending on the exact details of the application. In one embodiment, these summary channels are then calibrated to take minor measurable differences in manufacturing into account and to result in values in the appropriate scale and in the correct units. For example, if, during the manufacturing process, a particular temperature sensor was determined to have a slight offset, this offset can be applied, resulting in a derived channel expressing temperature in degrees Celsius.

[00142] For purposes of this description, a derivation or function is linear if it is expressed as a weighted combination of its inputs together with some offset. For example, if G and H are two raw or derived channels, then all derivations of the form A*G + B*H +C, where A, B, and C are constants, is a linear derivation. A derivation is non-linear with respect to its inputs if it can not be expressed as a weighted sum of the inputs with a constant offset. An example of a nonlinear derivation is as follows: if G > 7 then return H*9, else return H*3.5 + 912. A channel is linearly derived if all derivations involved in computing it are linear, and a channel is nonlinearly derived if any of the derivations used in creating it are nonlinear. A channel nonlinearly mediates a derivation if changes in the value of the channel change the computation performed in the derivation, keeping all other inputs to the derivation constant.

[00143] According to a preferred embodiment of the present invention, the algorithms that are developed using this process will have the format shown conceptually in Fig. 14. Specifically, the algorithm will take as inputs the channels derived from the sensor data collected by the sensor device from the various sensors, and demographic information for the individual as shown in box 1600. The algorithm includes at least one context detector 1605 that produces a weight, shown as Wl through WN, expressing the probability that a given portion of collected data, such as is collected over a minute, was collected while the wearer was in each of several possible contexts. Such contexts may include whether the individual was at rest or active. In addition, for each context, a regression algorithm 1610 is provided where a continuous prediction is computed taking raw or derived channels as input. The individual regressions can be any of a variety of regression equations or methods, including, for example, multivariate linear or polynomial regression, memory based methods, support vector machine regression, neural networks, Gaussian processes, arbitrary procedural functions and the like. Each regression is an estimate of the output of the parameter of interest in the algorithm, for example, energy expenditure. Finally, the outputs of each regression algorithm 1610 for each context, shown as Al through AN, and the weights Wl through WN are combined in a post-processor 1615 which outputs the parameter of interest being measured or predicted by the algorithm, shown in box 1620. In general, the post-processor 1615 can consist of any of many methods for combining the separate contextual predictions, including committee methods, boosting, voting methods, consistency checking, or context based recombination. [00144] Referring to Fig. 15, an example algorithm for measuring the energy expenditure of an individual is shown. This example algorithm may be run on sensor device 400 having at least an accelerometer, a heat flux sensor and a GSR sensor, or an I/O device 1200 that receives data from such a sensor device as is disclosed in co-pending United States Patent Application No. 10/682,759, the specification of which is incorporated herein by reference. In this example algorithm, the raw data from the sensors is calibrated and numerous values based thereon, i.e., derived channels, are created. In particular, the following derived channels, shown at 1600 in Fig. 30, are computed from the raw signals and the demographic information: (1) longitudinal accelerometer average, or LAVE, based on the accelerometer data; (2) transverse accelerometer sum of average differences, or TSAD, based on the accelerometer data; (3) heat flux high gain average variance, or HFvar, based on heat flux sensor data; (4) vector sum of transverse and longitudinal accelerometer sum of absolute differences or SADs, identified as VSAD, based on the accelerometer data; (5) galvanic skin response, or GSR, in both low and combined gain embodiments; and (6) Basal Metabolic Rate or BMR. Context detector 1605 consists of a naϊve Bayesian classifier that predicts whether the wearer is active or resting using the LAVE, TSAD, and HFvar derived channels. The output is a probabilistic weight, Wl and W2 for the two contexts rest and active. For the rest context, the regression algorithm 1610 is a linear regression combining channels derived from the accelerometer, the heat flux sensor, the user's demographic data, and the galvanic skin response sensor. The equation, obtained through the algorithm design process, is A*VSAD + B*HFvar+C*GSR+D*BMR+E, where A, B, C, D and E are constants. The regression algorithm 1610 for the active context is the same, except that the constants are different. The post-processor 1615 for this example is to add together the weighted results of each contextual regression. IfA 1 is the result of the rest regression and A2 is the result of the active regression, then the combination is just Wl *A1 + W2*A2, which is energy expenditure shown at 1620. In another example, a derived channel that calculates whether the wearer is motoring, that is, driving in a car at the time period in question might also be input into the post-processor 1615. The process by which this derived motoring channel is computed is algorithm 3. The post-processor 1615 in this case might then enforce a constraint that when the wearer is predicted to be driving by algorithm 3, the energy expenditure is limited for that time period to a value equal to some factor, e.g. 1.3 times their minute by minute basal metabolic rate.

[00145] This algorithm development process may also be used to create algorithms to enable the sensor device 400 to detect and measure various other parameters, including, without limitation, the following: (i) when an individual is suffering from duress, including states of unconsciousness, fatigue, shock, drowsiness, heat stress and dehydration; and (ii) an individual's state of readiness, health and/or metabolic status, such as in a military environment, including states of dehydration, under-nourishment and lack of sleep. In addition, algorithms may be developed for other purposes, such as filtering, signal clean-up and noise cancellation for signals measured by a sensor device as described herein. As will be appreciated, the actual algorithm or function that is developed using this method will be highly dependent on the specifics of the sensor device used, such as the specific sensors and placement thereof and the overall structure and geometry of the sensor device. Thus, an algorithm developed with one sensor device will not work as well, if at all, on sensor devices that are not substantially structurally identical to the sensor device used to create the algorithm. [00146] Another aspect of the present invention relates to the ability of the developed algorithms to handle various kinds of uncertainty. Data uncertainty refers to sensor noise and possible sensor failures. Data uncertainty is when one cannot fully trust the data. Under such conditions, for example, if a sensor, for example an accelerometer, fails, the system might conclude that the wearer is sleeping or resting or that no motion is taking place. Under such conditions it is very hard to conclude if the data is bad or if the model that is predicting and making the conclusion is wrong. When an application involves both model and data uncertainties, it is very important to identify the relative magnitudes of the uncertainties associated with data and the model. An intelligent system would notice that the sensor seems to be producing erroneous data and would either switch to alternate algorithms or would, in some cases, be able to fill the gaps intelligently before making any predictions. When neither of these recovery techniques are possible, as was mentioned before, returning a clear statement that an accurate value can not be returned is often much preferable to returning information from an algorithm that has been determined to be likely to be wrong. Determining when sensors have failed and when data channels are no longer reliable is a non-trivial task because a failed sensor can sometimes result in readings that may seem consistent with some of the other sensors and the data can also fall within the normal operating range of the sensor.

[00147] Clinical uncertainty refers to the fact that different sensors might indicate seemingly contradictory conclusions. Clinical uncertainty is when one cannot be sure of the conclusion that is drawn from the data. For example, the accelerometers might indicate that the wearer is motionless, leading toward a conclusion of a resting user, the galvanic skin response sensor might provide a very high response, leading toward a conclusion of an active user, the heat flow sensor might indicate that the wearer is still dispersing substantial heat, leading toward a conclusion of an active user, and the heart rate sensor might indicate that the wearer has an elevated heart rate, leading toward a conclusion of an active user. An inferior system might simply try to vote among the sensors or use similarly unfounded methods to integrate the various readings . The present invention weights the important joint probabilities and determines the appropriate most likely conclusion, which might be, for this example, that the wearer is currently performing or has recently performed a low motion activity such as stationary biking.

[00148] According to a further aspect of the present invention, a sensor device such as sensor device 400 may be used to automatically measure, record, store and/or report a parameter Y relating to the state of a person, preferably a state of the person that cannot be directly measured by the sensors. State parameter Y may be, for example and without limitation, calories consumed, energy expenditure, sleep states, hydration levels, ketosis levels, shock, insulin levels, physical exhaustion and heat exhaustion, among others. The sensor device is able to observe a vector of raw signals consisting of the outputs of certain of the one or more sensors, which may include all of such sensors or a subset of such sensors. As described above, certain signals, referred to as channels same potential terminology problem here as well, may be derived from the vector of raw sensor signals as well. A vector X of certain of these raw and/or derived channels, referred to herein as the raw and derived channels X, will change in some systematic way depending on or sensitive to the state, event and/or level of either the state parameter Y that is of interest or some indicator of Y, referred to as U, wherein there is a relationship between Y and U such that Y can be obtained from U. According to the present invention, a first algorithm or function fl is created using the sensor device that takes as inputs the raw and derived channels X and gives an output that predicts and is conditionally dependent, expressed with the symbol -[]-, on (i) either the state parameter Y or the indicator U, and (ii) some other state parameter(s) Z of the individual. This algorithm or function f 1 may be expressed as follows: fl(X) τ U + Z or fl(X) -"- Y + Z

[00149] According to the preferred embodiment, fl is developed using the algorithm development process described elsewhere herein which uses data, specifically the raw and derived channels X, derived from the signals collected by the sensor device, the verifiable standard data relating to U or Y and Z contemporaneously measured using a method taken to be the correct answer, for example highly accurate medical grade lab equipment, and various machine learning techniques to generate the algorithms from the collected data. The algorithm or function fl is created under conditions where the indicator U or state parameter Y, whichever the case may be, is present. As will be appreciated, the actual algorithm or function that is developed using this method will be highly dependent on the specifics of the sensor device used, such as the specific sensors and placement thereof and the overall structure and geometry of the senor device. Thus, an algorithm developed with one sensor device will not work as well, if at all, on sensor devices that are not substantially structurally identical to the sensor device used to create the algorithm or at least can be translated from device to device or sensor to sensor with known conversion parameters. [00150] Next, a second algorithm or function f2 is created using the sensor device that takes as inputs the raw and derived channels X and gives an output that predicts and is conditionally dependent on everything output by fl except either Y or U, whichever the case may be, and is conditionally independent, indicated by the symbol -"-, of either Y or U, whichever the case may be . The idea is that certain of the raw and derived channels X from the one or more sensors make it possible to explain away or filter out changes in the raw and derived channels X coming from non- Y or non-U related events. This algorithm or function E may be expressed as follows:

G(X) τ Z and (E(X) ^ Y or G(X) ^- U [00151] Preferably, £2, like fl, is developed using the algorithm development process referenced above. £2, however, is developed and validated under conditions where U or Y, whichever the case may, is not present. Thus, the gold standard data used to create f2 is data relating to Z only measured using highly accurate medical grade lab equipment. [00152] Thus, according to this aspect of the invention, two functions will have been created, one of which, fl, is sensitive to U or Y, the other of which, £2, is insensitive to U or Y. As will be appreciated, there is a relationship between f 1 and f2 that will yield either U or Y, whichever the case may be. In other words, there is a function G such that f3 (fl , f2) = U or f3 (f 1 , £2) = Y. For example, U or Y may be obtained by subtracting the data produced by the two functions (U = fl-£2 or Y = fl-£2). In the case where U, rather than Y, is determined from the relationship between fl and £2, the next step involves obtaining Y from U based on the relationship between Y and U. For example, Y may be some fixed percentage of U such that Y can be obtained by dividing U by some factor.

[00153] One skilled in the art will appreciate that in the present invention, more than two such functions, e.g. (fl, £2, β, ...f_n-l) could be combined by a last function f_n in the manner described above. In general, this aspect of the invention requires that a set of functions is combined whose outputs vary from one another in a way that is indicative of the parameter of interest. It will also be appreciated that conditional dependence or independence as used here will be defined to be approximate rather than precise.

[00154] It is known that total body metabolism is measured as total energy expenditure (TEE) according to the following equation:

TEE = BMR + AE + TEF + AT, wherein BMR is basal metabolic rate, which is the energy expended by the body during rest such as sleep, AE is activity energy expenditure, which is the energy expended during physical activity, TEF is thermic effect of food, which is the energy expended while digesting and processing the food that is eaten, and AT is adaptive thermogenesis, which is a mechanism by which the body modifies its metabolism to extreme temperatures. It is estimated that it costs humans about 10% of the value of food that is eaten to process the food. TEF is therefore estimated to be 10% of the total calories consumed. Thus, a reliable and practical method of measuring TEF would enable caloric consumption to be measured without the need to manually track or record food related information. Specifically, once TEF is measured, caloric consumption can be accurately estimated by dividing TEF by 0.1 (TEF = 0.1 * Calories Consumed; Calories Consumed = TEF/0.1). [00155] Preferably, the sensor device is in communication with a body motion sensor such as an accelerometer adapted to generate data indicative of motion, a skin conductance sensor such as a GSR sensor adapted to generate data indicative of the resistance of the individual's skin to electrical current, a heat flux sensor adapted to generate data indicative of heat flow off the body, a body potential sensor such as an ECG sensor adapted to generate data indicative of the rate or other characteristics of the heart beats of the individual, a free-living metabolite sensor adapted to measure metabolite levels such as glucose and/or lactate, and a temperature sensor adapted to generate data indicative of a temperature of the individual's skin. In this preferred embodiment, these signals, in addition the demographic information about the wearer, make up the vector of signals from which the raw and derived channels X are derived. Most preferably, this vector of signals includes data indicative of motion, resistance of the individual's skin to electrical current and heat flow off the body.

[00156] Conventional thinking in the field of cardiology/ECG is that an ECG signal must be measured across the heart, meaning with electrodes placed in two different quadrants of the heart's conventionally defined sagittal and transverse planes. A device and methodology are disclosed herein which permits the measurement of an ECG signal from certain pairs of points located within regions or areas of a mammalian body previously considered inappropriate for such measurement. The device and methodology disclosed herein focus on the identification of certain locations on the body of any mammal within the previously defined equivalence regions utilized for electrode location. Many of these electrode locations are within a single quadrant , i.e., when the electrode locations are connected geometrically directly through tissue, the line described thereby does not cross into another quadrant. In other words, certain points within one quadrant are correlated with the electropotential of the ECG signal conventionally associated with a different quadrant because the potential from the opposite side has been transported to that point internally through what appear to be low impedance non-homogeneous electropotential or electrical pathways through the body, which may be analogized as internal signal leads within the tissue. This methodology therefore focuses on two different aspects of the ECG signal, rather than more narrowly defining these aspects as emanating from certain quadrants of the body. Thus, contrary to the teachings of the prior art, an ECG signal may be detected and measured using pairs of electrodes placed within a single quadrant , but detecting a significant electrical potential difference between the two points. In other words, the two points are inequipotential with respect to one another. In most instances, it is more helpful to envision the electrode locations being located within independent regions of skin surface, separated by a boundary which may be planar or irregular.

[00157] In the preferred embodiment of the present invention, pairs of locations on or near the left arm have been identified for placement of electrodes to detect the different aspects of the ECG signal. It is to be noted that similar sites within equivalence regions are found at a myriad of locations on the mammalian body, including the right and left arms, the axillary area under the arms, the anterior femoral area adjacent the pelvis, the back of the base of the neck and the base of the spine. More specifically, certain locations on the left arm carry an aspect of the ECG signal and certain locations on or near the left arm carry a different aspect of the ECG signal. It is also to be specifically noted that anatomical names, especially names of muscles or muscle groups, are used to identify or reference locations on the body, though placement of the electrodes need only be applied to the skin surface directly adjacent these locational references and are not intended to be invasive. Referring now to Figs. 19A and 19B, which are drawings of the back and front of the left arm, respectively, the inventors have found that the left wrist 1905 , left triceps muscle 19110, and the left brachialis muscle 1915 are locations that, when paired with locations surrounding the deltoid muscle 1920, the teres major muscle 1925 and the latissimus dorsi muscle 1930, can produce an electrical potential signal that is related to the conventional signal measured between two quadrants. More specifically, the signal from these pairs of points on the left arm correlates with the QRS complex associated with the contraction of the ventricles.

[00158] Thus, by placing one electrode on the wrist 195 , triceps muscle 1910 or the brachialis muscle 1915 and a second electrode on the deltoid muscle 1920, the teres major muscle 1925 or the latissimus dorsi muscle 1930, it is possible to detect the action potential of the heart and thus an ECG signal. The electrodes are preferably located near the central point of the deltoid and tricep muscles, are spaced approximately 130 mm and more particularly 70-80 mm apart and tilted at approximately 30-45 degrees toward the posterior of the arm from the medial line, with 30 degrees being most preferred. While certain specific preferred locations on or near the left arm have been described herein as being related to the electropotential of the second aspect of the ECG signal, it should be appreciated those locations are merely exemplary and that other locations on or near the left arm that are related to the electropotential of the second aspect of the ECG signal may also be identified by making potential measurements. It is further to be specifically noted that the entire lower arm section 5 ' is identified as providing the same signal as wrist 1905. Referring now to Fig. 19C, four specific pairs of operative locations are illustrated, having two locations on the deltoid 20 and two locations on the various aspects of the tricep 1910. In one embodiment, the placement location is the juncture of the bicep and deltoid meet. The second electrode may then be placed anywhere on the deltoid. It is to be noted that the dashed lines between the locations indicate the operative pairings and that the solid and white dots represent the relative aspects of the ECG signal obtainable at those locations. Four possible combinations are shown which provide two aspects of the ECG signal. An inoperative pair, 1913 is illustrated to indicate that merely selecting particular muscles or muscle groups is not sufficient to obtain an appropriate signal, but that careful selection of particular locations is required. [00159] In another embodiment, pairs of locations on or near the right arm for placing electrodes to detect an ECG signal are identified. Referring to Figs. 2OA and 2OB, the base of the trapezius 1935, pectoralis 2040 and deltoid 2020 are locations that are related to the electropotential of the second aspect of the ECG signal, meaning that those locations are at a potential related to the heart's conventionally defined right side action potential. Tricep 1910, especially the lateral head area thereof, and bicep 2045 are locations that are related to the electropotential of a first aspect of the ECG signal, meaning that those locations are at a potential related to the heart's conventionally defined left side action potential, even though those locations are in quadrant III. Thus, as was the case with the left arm embodiment described above, by placing one electrode on the tricep 10 and a second electrode on the deltoid 1920, it is possible to detect the action potential of the heart and thus an ECG signal. Again, while certain specific preferred locations on or near the right arm have been described herein as being related to the electropotential of the first aspect of the ECG signal, it should be appreciated that those locations are merely exemplary and that other locations on or near the right arm that are related to the electropotential of the first aspect of the ECG signal may also be identified by making potential measurements.

[00160] Referring now to Figs. 20C, 2OD and 2OE, a series of electrode pair locations are illustrated. In Figs. 30C and 2OD, the conventionally defined sagittal plane 2 and transverse plane 3 are shown in chain line generally bisecting the torso. Each of the operative pairs are identified, as in Fig. 19C by solid and white dots and chain line. Inoperative pairs are illustrated by X indicators and chain line. As previously stated, inoperative pairs are illustrated to indicate that mere random selection of locations, or selection of independent muscle or muscle groups is insufficient to locate an operative pair of locations. The specific locations identified as within the known operative and preferred embodiments are identified in Table 4 as follows: Table 4

Figure imgf000052_0001
[00161] Similarly, it should be understood that the present invention is not limited to placemnt of pairs of electrodes on the left arm or the right arm for measurement of ECG from within quadrants I or III, as such locations are merely intended to be exemplary. Instead, it is possible to locate other locations within a single quadrant. Such locations may include, without limitation, pairs of locations on the neck, chest side and pelvic regions, as previously described, that are inequipotential with respect to one another. Thus, the present invention should not be viewed as being limited to any particular location, but instead has application to any two inequipotential locations within a single quadrant.

[00162] One of the primary challenges in the dete