JP2003190109A - Autonomic nervous system function evaluating method and system therefor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enable a user to use the measurement of the signals of heartbeats conveniently and easily at low cost. <P>SOLUTION: In the method automated for evaluating the heart rate variation (HRV) by remote control, signals defining the heartbeats of the user are collected and digitized by the client of a client/server system. The digitized signals are sent through a network system to a server and the digitized signals are analyzed on-line by an ANS function analytic system automated for the purpose of applying the indices of autonomic nerves. The indices are sent to the user (client) and/or a medical expert for further confirmation or assistance. <P>COPYRIGHT: (C)2003,JPO

Description

Detailed Description of the Invention

[0001]

FIELD OF THE INVENTION The present invention relates to a method and system for assessing the function of the autonomic nervous system, and more particularly to an automated remote control method and system for measuring heart rate variability. It is about.

[0002]

The Autonomic Nervous System (ANS) consists of two parts, the sympathetic and parasympathetic parts. Most organs receive action potentials from both parts and usually under the environment, and both parts
Collaborate to meet life demands and favorable organ function. Problems, including both chronic and acute illness, occur when the autonomic nervous system goes out of balance. For example coronary heart disease, hypertension and sudden death. Even low-risk conditions such as palpitations, dyspepsia, dyspnea and insomnia are believed to be associated with autonomic imbalance.

Many techniques have been successfully developed to assess the function of the autonomic nervous system. These techniques include heart rate changes associated with deep breathing, the Valsalva response,
Includes sudomotor function, orthostatic blood pressure recording, cold pressor tests and biochemical tests. However, these techniques are mostly intrusive and require expensive diagnostic equipment. Therefore, these techniques are not suitable for general use.

Recent sophisticated advances in computer hardware and software have led to the development of various techniques for diagnosing autonomic nervous system function. Heart rate variability (HR) is a measure of beat-to-beat changes in heartbeat.
V) was developed as a functional index for the autonomic nervous system. HRV is an important development as this technology is non-intrusive. The HRV study is prevented from inflicting any inadequacy on the patient or other parties. Also, the hardware for this technology is inexpensive and widely applied. Furthermore, animal and clinical studies that accurately confirm HRV reflect sympathetic and parasympathetic activities and their balance.

In adults, there are about 70 heartbeats per minute at rest. These rhythmic heartbeats result from electrical event coupling between cardiomyocytes. The heart receives action potentials from both the sympathetic and parasympathetic parts of the autonomic nervous system, which normally cooperate to control the homeostasis of the body. However, if the body is stressed, the sympathetic nervous system becomes predominant due to elevated heart rate and blood pressure. Beyond that emergency, the parasympathetic system replaces and the heartbeat decreases.

Even under resting conditions, the heartbeat of a healthy body shows periodic fluctuations. These periodic variations can be fast, slow, regular or irregular. In addition, the amplitude of these fluctuations is rather small, and as a result, it is difficult to detect using conventional analysis methods. However, with the recent advances in analysis tools from the field of electronics, better evaluation of HRV by frequency domain analysis (which is EC
Based on the arithmetic operations performed on the G-lead data).

Researchers have found that based on frequency analysis, HRV
Have found that it can be characterized into two major components: a high frequency (HF) component and a low frequency (LF) component. It is known that high frequency components are synchronized with breathing and occur in healthy bodies every 3 seconds. The exact origin of the low frequency components is still under study. It is probably associated with vascular activity or baroreflex and occurs approximately every 10 seconds. Further, a researcher classifies low frequency components into low frequency components in a narrow sense and ultra low frequency components. The LF / HF ratio depends on the sympathetic vagus nerve (sympathov
agal) It is thought that it reflects balance or reflects sympathetic modulation (Akselrod et al., 1981; Malliani et al., 1991), whereas researchers are currently investigating high frequency (HF).
Or agree that the total output (TP) indicates parasympathetic control of heartbeat. In addition to being an indication of autonomic nervous system functioning, evidence is provided that HRV reflects other pathological conditions.

The reduced HRV appears to be a sign of increased intracranial pressure (Lowensohn et al., 1977). In addition, other studies point out that if HRV in the elderly is lowered by some standard deviation, its rate of motility is 1.7 times higher than in normal individuals (Tsjui et al., 1994).
Applicant's previous work has demonstrated that brain death eliminates LF (Kuo et al., 1997). Also, the effects of aging and gender have been shown to affect heart sympathetic and parasympathetic control (Kuo et al., 1999). In the field of gynecology, pregnant women are reported to have increased sympathetic function. Smaller effective parasympathies and larger dominant sympathies of heart beat have been demonstrated to be elevated in preeclamptic pregnancies (Yang et al., 2000).

[0009]

[Problems to be Solved by the Invention] Autonomic nervous system (ANS)
Function provides a meaningful reflex of many physiological states, and the acquisition of information on ANS function by measuring heart rate variability (HRV) has not been conveniently provided to users with conventional HRV technology. Currently, in order to obtain ANS function information, the user needs to be in a facility where an electrocardiogram (ECG) module is available. Furthermore, the acquisition of ECG signals requires the application of multiple electrodes at the correct locations throughout the body. Also, the electrocardiographic signal needs to be processed and analyzed by a trained technician to give a meaningful ANS indicator. The applications of the prior art cannot be widely applied because such techniques are not user friendly and cannot be easily used by the patient.

Therefore, the present invention is for obtaining an autonomic nervous system index by measuring heartbeat variability, and the measurement of heartbeat signals can be conveniently used by the user at low cost. Methods and systems are provided.

[0011] Meaningful results are also provided promptly to the user and / or medical professionals for further confirmation and assistance, potential adverse consequences are reduced, and the user's remaining power. Is increased.

[0012]

The present invention provides a method for assessing the function of the autonomic nervous system, wherein physiological signals revealing the cardiac cycle are collected at the first end. The digitized physiological signal is sent to the second end through the network system, and the digitized physiological signal is automatically analyzed so as to give an autonomic nervous system index. Further, the method provides the analyzed results to the user and / or medical professional through the network system. Also, the physiological signal that reveals the cardiac cycle collected at the first end according to the present invention is an ECG signal by using an existing physiological function monitoring system, an inexpensive personal computer, a PDS, a mobile phone or a microchip, Includes blood pressure signals, blood flow signals, local blood volume signals, local oxygen saturation signals and heart sound signals.

The present invention provides an automated remote control system for assessing autonomic nervous system function. The automated remote control system comprises a heartbeat signal acquisition system that collects heartbeat signals and a diagnostic system that analyzes the heartbeat signals to provide an indication of autonomic nervous system function. further,
The automated remote control system includes a network system that transmits a heartbeat signal from the heartbeat signal acquisition system to the diagnostic system and transmits an indicator of autonomic nervous system function from the diagnostic system to the heartbeat signal acquisition system.

Thus, the acquisition of physiological signals revealing the cardiac cycle is collected at the first end, while the analysis of the physiological signals to obtain meaningful indicators is automatically performed at the second end. . Thus, the collection of physiological signals is easily and inexpensively performed by the user. In other words, this heart rate variability technique is readily available to patients or even normal subjects. As a result, autonomic nervous system data are submitted in large quantities to facilitate advances in the quest for functional relationships between heart rate variability and various pathological conditions.
Also, the user may be immediately alerted to his or her health status to prevent adverse consequences.

Both the foregoing general description and the following detailed description are exemplary and provide a further description of the invention.

[0016]

BEST MODE FOR CARRYING OUT THE INVENTION Currently, the majority of commercialized physiological function monitoring devices, for example, blood pressure, blood flow, local blood volume,
Devices for monitoring local oxygen saturation and heart sounds have already provided digitized signals. These various types of signals carry cardiac cycle information that can be used to determine heart rate variability. With the advancement of Internet technology, the Internet is available for information transferred in client-server systems. A device that can provide heartbeat information is regarded as a client, and a server
Previously developed automated HRV analysis technology (Kuo et al., 199
By using (9), (ECG, blood pressure, blood flow,
It provides a service to instantaneously convert raw physiological signals (including local blood volume, local oxygen saturation and heart sounds) into meaningful autonomic nervous system indicators. As a result, ANS
Functional data can be submitted in large quantities to facilitate the evolution of the search for functional relationships between HRV and various physiological conditions. Moreover, HRV technology is widely used and may be readily available anywhere to even patients or even normal subjects.

In general, heart rate variability is derived from the ECG (electrocardiogram), which is an electrical record of myocardial contraction. E
In addition to CG, there are blood pressure signals, blood flow signals, local blood volume signals, local oxygen saturation signals, heart sound signals, and secondary signals derived from the above signals that also include cardiac cycle information. is there. These physiological signals, although not conventionally used to determine HRV, can be used as a source of HRV to extend the wider application of HRV technology.

FIG. 1 is a flow chart showing a method for diagnosing the function of the autonomic nervous system according to the present invention. As shown in FIG. 1, an electrocardiogram or signal flow such as blood pressure, blood flow, local blood volume, local oxygen saturation and heart sounds is collected by electrodes, transducers or microphones, for example for 5 minutes. (Step 100). Subsequently, the physiological signal for 5 minutes is amplified and filtered by a bandpass filter (step 102). Further, the processed signal is transmitted to an A / D converter having a sampling rate of 256-2048 Hz (step 104). Data sampling is performed with a computing device that includes at least a microprocessor and sufficient memory. After this, the digitized signal is
It is compressed (step 106) and sent to the server over the network for online analysis.

Information transmitted from the client to the server passes through either a local area network or the Internet via cables, optical fibers or radio waves (step 108).

After the server receives digitized physiological signals such as ECG, blood pressure, blood flow, local blood volume, local oxygen saturation and heart sounds, the digitized signals are first decompressed. (Step 110), and then analyzed to estimate heart rate variability. The spike detection algorithm (Kuo and Chan, 1992) is used to detect all peaks in the digitized physiological signal. The peak of each heart beat is defined as the time point of that heart beat (step 112), and the interval between the two peaks is estimated as the beat-to-beat interval between the current and the subsequent heart beats (step 114). Parameters such as duration and amplitude of all peaks are measured such that their mean and standard deviation can be calculated as a standard template. Subsequently, each heart beat is compared and validated with a standard template. If any of the peak interval values has a standard score greater than 3, it is rejected as an error.

The enabled peak spacing values are then resampled and interpolated at a rate of 7.11 Hz to be continuous in the time domain (step 116). Then, frequency domain analysis is performed using an FFT (fast Fourier transform) (step 118). The DC component of the signal is detected and the Hamming window is used to reduce the leakage effect. During each 288 seconds or 2048 data points, the power spectral density is estimated based on the FFT. The resulting power spectrum is corrected for the attenuation results from the sampling and Hamming windows. Subsequently, the power spectrum has a low frequency (LF0.
04-0.15Hz) and high frequency (HF0.15-
(0.40 Hz), total power (TP) and ratio of low frequency to high frequency (LF / HF) are quantified by means of integration into standard frequency domain parameters (step 120).

The analyzed results, including various ANS indicators, frequency spectra and recommendations, are sent to the client through the network (step 122).

The user should immediately pay attention to his / her health condition. If they have too high or too low a dysfunction in sympathetic and / or parasympathetic functions, the server will automatically notify the doctor or other health care professional to provide immediate help to the user. Can be planned. According to this embodiment, the potential adverse consequences are reduced, with quick diagnosis and information transfer and easy availability of the HRV measurement to the user,
And the remaining power of the user is enhanced.

2 to 4 are views showing a website display for an automated remote control ANS function diagnostic system according to the present invention. As shown in FIGS. 2-4, the client computer system is connected to the server website after collecting the ECG signals. The computer file containing the ECG signal is then sent to that server. Within a few seconds, the server includes HF for parasympathetic functions and LF / HF for sympathetic functions.
Respond with an NS index. In addition, the server provides the client with a raw frequency spectrum of parameters characterizing the HRV for offline verification.

Many existing physiological function monitors digitize physiological signals such as ECG, blood pressure, blood flow, local blood volume, local oxygen saturation and heart sounds, and these digitized signals are digitized. It already provides the ability to process and provide transient or average heart rate information to the user. If these digitized signals are sent via the Internet to an automated, remotely controlled ANS function diagnostic server, the user's ANS function is readily available within seconds. The digital physiological function monitoring device for measuring the heartbeat related signal can be used as an automated remote control ANS function diagnostic system, and the design of additional software except that these devices include network input / output capability. Provided without having.

6 and 7 show a comparison between the conventional method of diagnosing ANS function using the electrocardiogram signal as the physiological signal and the method of the present embodiment. As shown in Figures 6 and 7, the client is only responsible for data acquisition and data output. No analysis of the data is required at the client. Therefore, only inexpensive hardware, such as physiological monitoring systems, personal computers, PDAs (personal digital assistants), mobile phones or microchips are required of the client. As a result, the data related to the ANS function is
Can be submitted in large quantities to facilitate the development of the search for functional relationships between and various pathological conditions. Moreover, the method is widely used and may be readily available anywhere, even to patients or normal subjects.

In FIG. 7, the server is designed to provide rapid data analysis, for example by using a high speed computer according to an automated heartbeat analysis algorithm (Kuo et al., 1999). By sending various ANS indicators converted from raw pathological signals to the client, accurate and prompt service is provided to the user, and therefore the existing physiological monitoring device diagnoses the function of the autonomic nervous system. With additional features for As a result, the automated remote controlled autonomic nervous system functional diagnostic system of this embodiment is significantly reduced.

Also, since the heartbeat signal of this embodiment is derived from measurements including blood pressure, blood flow, local blood volume, local oxygen saturation, heart sound, and ECG, the HRV technique is more widely applied. obtain. In practice, some of these measurements are performed on a personal computer, P
It can be easily executed by the user (client) at the user's home by using inexpensive hardware such as DA and microchip. Thus, diagnosing ANS function is very low cost, readily available, and convenient for the user. In addition, the present embodiment provides an automated HRV function analysis system (server) to receive and analyze a heartbeat signal from a user through a network system and quickly provide a meaningful ANS index to the user. To do. The user can immediately pay attention to his / her health condition. The server can be designed to automatically notify doctors or other health care professionals to help their users, so that if sympathetic and / or parasympathetic function is compromised, there is a potential disadvantage. The result is reduced and the user's remaining power is increased.

It will be apparent to those skilled in the art that various changes and modifications can be made to the structure of the present invention without departing from the scope and spirit of the invention. In view of the above, the present invention includes modifications and variations of the present invention provided they are within the scope of the present invention.

[0030]

According to the present invention, there is provided a method and system for obtaining an autonomic nervous system index by measuring heartbeat variability, and measuring a heartbeat signal at low cost, which is convenient and easy for the user to use. Can be provided.

[Brief description of drawings]

FIG. 1 is a flow diagram illustrating a method of diagnosing autonomic nervous system function according to an embodiment of the present invention.

FIG. 2 is a diagram showing a website display for an automated remote control ANS function diagnostic system according to the present invention.

FIG. 3 is a diagram showing a website display for an automated remote control ANS function diagnostic system according to the present invention.

FIG. 4 is a diagram showing a website display for an automated remote control ANS function diagnostic system according to the present invention.

FIG. 5 is a diagram showing an example of frequency spectra of various parameters that characterize heartbeat variability.

FIG. 6 is an explanatory diagram of a conventional method of diagnosing ANS function using an electrocardiogram signal as a physiological signal.

FIG. 7 is an explanatory diagram of the present embodiment for diagnosing ANS function by using an electrocardiogram signal as a physiological signal.

[Explanation of symbols]

100 ECG 102 amplification and filtering 104 A / D converter 106 compression 110 stretch 112 Heartbeat identification 114 beats-estimation of beat intervals 116 Sample & Hold 118 FFT 120 ANS indicators

Claims (4)

[Claims] 1. A heart rate variability is analyzed by collecting a physiological signal that reveals a heartbeat at a first end, sending the physiological signal to a second end through a network system, and analyzing the physiological signal at the second end. A method for evaluating an autonomic nervous system function, which comprises automatically quantifying a frequency domain parameter of the physiological signal so as to characterize it. 2. Analyzing the digitized physiological signal at the second end comprises executing an automated heartbeat variability analysis algorithm, wherein beat-based analysis is performed based on the digitized physiological signal. The autonomic nervous system function evaluation method according to claim 1, wherein a beat interval value is estimated, the interval value is converted into a frequency spectrum, and a component of the frequency distribution of the heartbeat variability is quantified. 3. An automated remote control system for evaluating autonomic nervous system function, comprising: a heartbeat signal acquisition system for collecting heartbeat signals; and an analysis of the heartbeat signals, the frequency domain parameter of the heartbeat signals. A quantified to provide an index of autonomic nervous system function, transmitting the heartbeat signal from the heartbeat signal acquisition system to the diagnosis system for online analysis, and the heartbeat signal acquisition system from the diagnosis system And a network system for transmitting the index of the autonomic nervous system function to the autonomic nervous system function evaluation system. 4. The diagnostic system automatically estimates a beat-beat interval value between the current and subsequent heart beats, converts the interval value into a frequency spectrum, and quantifies the components of the frequency distribution of heart rate variability. The autonomic nervous system function evaluation system according to claim 3, further comprising an automated ANS function analysis system for performing the function evaluation.