Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

• the invention relates to the detection of the risk for a patient for having a torsade de pointes event, and the mechanisms underlying such risk, in particular via the use of neural networks.
• Torsades de Pointes is an arrhythmia that can deteriorate to ventricular fibrillation and sudden death. It occurs essentially in the congenital long QT syndromes or as a rare side effect of QT-prolonging cardiac and non-cardiac drugs.
• the current detection strategy is based on the QT measurements (i.e. , interval between the beginning of the QRS complex and the end of the T wave, traditionally, using generally the lead II of the electrocardiogram (ECG)).
• QT measurements i.e. , interval between the beginning of the QRS complex and the end of the T wave, traditionally, using generally the lead II of the electrocardiogram (ECG)
• ECG electrocardiogram
• Sotalol is a potassium-current inhibitor drug, mainly used to prevent atrial fibrillation, angina pectoris and hypertension. Sotalol can lead to a prolongation of the ventricular repolarization and, rarely (18-4.8%), to TdP.
• CNN convolutional neural networks
• Attia et al., 2018 tried to estimate plasma drug concentration using CNN by analyzing 10-s recordings of 42 patients that received Dofetilide (antiarrhythmic drug) or placebo. These recordings were recorded at specific time points before and after drug consumption. In this experiment the authors used the data from two distinct prospective randomized controlled trials available in the Physionet repository. In their regression analysis, using CNN, they reached a correlation of 0.85. However, the database was relatively small (42 subjects) and no cross- validation in training was used, leading to possible overfitting.
• the strategy developed by the inventors was to develop a model that is able to detect the intake of sotalol (which is a marker of the risk to have a torsade de pointes).
• sotalol which is a marker of the risk to have a torsade de pointes.
• the inventors were able to show that it is able to discriminate between congenital long QT syndromes (an inherited disorder of cardiac repolarization that predisposes to syncope and to sudden death from polymorphic ventricular tachycardia, i.e TdP), and in particular to identify LQT2 patients vs. LQT1 or LQT3 patients (for which the biological mechanisms at hand are different).
• the risk mentioned above is not an absolute risk, but rather a relative risk.
• the patient detected“at risk” in the context of the present invention presents a higher risk than the population as a whole.
• the patient presents characteristics that are shared by other patients who have recorded a TdP episode. This means that the risk is linked to the sensitivity of detection of the fact that the patient will have TdP.
• the test since the test is not 100% specific, some patients may present the characteristics and not have a TdP episode (false positive) and since the test is not 100% sensitive, there may be some patients who don’t have the characteristics and that will develop a TdP event (false negative).
• the quality of the test is represented by the Area Under Received Operating Curve (AUROC).
• the methods and systems herein disclosed are able to classify the patient in classes (increased risk / no increased risk) and in subclasses pertaining to the type of risk (or mechanism by which the risk is present) for patients in the increased risk class (drug induced risk, acquired risk, congenital long QT syndrome and type thereof).
• the invention relates to a method for producing a machine learning algorithm (or machine-learning system) capable of predicting or estimating the risk for a patient to have a torsade de pointes event, and preferably the cause (or underlying mechanism) thereof, comprising:
• the inventors have shown that using such system makes it possible to detect whether the patient will have an episode of torsade de pointes in a short delay (within 48 hours or 24 hours).
• the inventors also showed that the system can also be operated using ECG obtained from a single lead.
• the output of the system that is trained may rely on a classification technique, which provides a prediction, probability or likelihood, rather than certainty, that the patient has a certain condition (here to risk a TdP event, and the nature of the biological mechanism).
• a classification technique which provides a prediction, probability or likelihood, rather than certainty, that the patient has a certain condition (here to risk a TdP event, and the nature of the biological mechanism).
• classifiers using one or more of the following techniques may be used to determine the likelihood of such risk: linear discriminant analysis, neural network, clustering techniques, support vector machine, logistic regression, naive Bayes, random forest, decision tree, etc.
• the machine learning classifier is an artifi cal neural network, and preferably a convolutional neural network.
• the invention relates to a method for producing an artificial neural network system capable of predicting or estimating the risk for a patient to have a torsade de pointes event, and preferably the cause (or underlying mechanism) thereof, comprising:
• the neural network allows to obtain models that are able to determine whether an ECG of a patient presents the same characteristics than the ECG of patients at risk of having a TdP event. Such models can thus be used for implementing various methods as disclosed below.
• the invention thus relates to a computer-implemented electrocardiogram (ECG) analysis method comprising:
• the output provides an estimate of the risk of having a torsade de pointes event and underlying mechanism of such risk.
• the invention also relates to a computer-implemented electrocardiogram (ECG) analysis method comprising:
• the machine-learning model is a neural network, such as a feedforward neural network, a convolutional neural network, or a recurrent neural network, this list being non limitative. It is preferably a convolutional neural network.
• Step a. corresponds to the extraction of a segment (part) from an ECG signal
• a data segment that can be used for processing in a machine-learning system that has previously been trained as disclosed above. It can be done online or offline.
• step b. it is possible to normalize and/or to standardize the data segment or no processing at all.
• the standardization can be performed using the mean of the signals acquired for each lead for the patient or per each lead. Normalization may be performed using the ECG signals of all patients that are present in a reference database or for each ECG.
• step c. the data segment is applied to the machine-learning algorithm that has been developed as disclosed above, in order to obtain an output.
• Other data can also be applied together with the data segment (such as sex, age of the patient, kaliemia, etc).
• the data from all the leads can be entered at once as the ECG signal (composite ECG signal) or part of or all leads separately.
• the invention also relates to a method for estimating the risk for a patient to have a torsade de pointes event and underlying mechanism thereof, comprising: a. receiving, by a processing device, signal data representing a time segment of an ECG waveform of a subject patient b. analyzing, by the processing device via a configured artificial machine learning algorithm (in particular a neural network), said signal data to generate an output of the artificial neural network,
• the output is the likelihood of the risk for the patient to have a tosade de pointes event, and the cause (underlying mechanism) thereof.
• the output provides a likelihood of the patient belonging to the classes that were defined during training.
• the output will provide a likelihood of being in one or the other class
• the output will provide a likelihood for the patient of being in each of the five classes. It is clear that the output will likely favor one class indicating that the patient is probably belonging to this class.
• the risk of having a torsade de pointes event is linked to the belonging, for the patient, to a class in which patients are more at risk.
• the risk is a relative risk as compared to patients classified as control.
• the risk of having a TdP event is considered to be increased (as compared to control patients) when the ECG of the patient is classified like an ECG of a patient belonging to a class of patient with increased risk (patient taking a drug that increases the QT, or patient with a long QT syndrome).
• the machine learning algorithm is also able to discriminate the class and thus to provide information as to the underlying mechanism that increases the TdP risk: inhibition of IKr channel for patients taking sotalol or LQT-2 patients, mutation in the KvLQTI gene (also known as KCNQ1) involved in the IKs channel for LQT-1 patients, mutation in SCN5A, the gene encoding the sodium channel INa/INa late for LQT-3 patients.
• the invention also relates to a cardiac monitoring device comprising: a. at least one electrode for obtaining an electrocardiogram (ECG) signal from a patient;
• ECG electrocardiogram
• a processing unit comprising at least one processor operatively coupled to the at least one electrode;
• At least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to:
• the standardization / normalization of the ECG signal in ii. is optional and can be performed as disclosed above.
• Step iii. Aims at using only the signal for a specific duration (generally 10 seconds) in the machine learning classifier. This step is optional and is not always necessary.
• the machine learning classifier is preferably a neural network, in particular a convolutional neural network.
• the invention also pertains to a system for the evaluation of the risk for a patient to have a torsade de pointe event, comprising:
• an external defibrillator configured to monitor electrocardiogram (ECG) data from the patient
• processors configured to perform operations comprising:
• the ECG data is the vector formed by all these multiple ECG signals from the different leads.
• the examples provide an illustration of such vector for 8 leads.
• the defibrillator or monitor comprising the at least one electrode and the computer/processor or processing unit can be at different locations.
• the ECG signal is sent to the processing unit or processor in order for the machine learning classifier to process it. It is also possible to send the output to the defibrillator or monitor or to another device (such as a smartphone).
• the invention also makes it possible to evaluate the torsadogenic potential of a drug, of a product or of a composition of interest.
• the torsadogenic potential of a substance is the potential of this substance to induce a torsade de pointes after administration in a patient.
• the url address of the guidelines is:
• the QT interval is an imperfect biomarker for the risk of proarrhythmic effect.
• the examples show that the AUC obtained when using QT for detecting patient having had sotalol is 0.76, which proves that improvement can be obtained.
• the machine learning classifier defined above makes it possible to detect whether such risk is present, by analyzing the ECG of patients having been administered the compound of interest.
• the invention thus relates to an ex vivo method for determining the risk for a substance to induce a torsadogenic effect after administration to a patient, comprising the following steps:
• the substance presents a risk of induction of a torsadogenic effect if a risk for the patient to have a torsade de pointe event is obtained after administration of the substance.
• ECG signals for the patient acquired before administration of the substance of interest.
• the method disclosed above is repeated on a cohort of patients containing a number of patients greater than or equal to 2. Carrying out these studies on a sufficient number of patients thus makes it possible to obtain results which present a statistical reality for the molecule that it is desired to test (drug of interest), eliminating the inter-patient variations.
• a placebo negative control
• a positive control such as sotalol at 80mg
• Sotalol at 80 mg is indeed preferably used as positive control. This molecule, at this concentration, has many advantages:
• the maximum concentration is well known (approximately three hours after oral administration), which makes it possible to easily plan the moment at which to take the measurements measured above.
• This molecule is known to inhibit the IKr channel.
• This molecule at this concentration, does not induce an episode of torsade de pointes, and is therefore safe for the patient. It is however known that this molecule, at its clinical used doses (used up to 320 mg), can induce torsades de pointes.
• the machine learning classifiers herein disclosed are particularly adapted to detect intake of sotalol (80 mg) in patients with an AUC of 0.99 (Cl [0.98; 1 ]).
• sotalol 80 mg
• AUC 0.99
• the above steps are carried out for each patient of a cohort of patients, and the risk is thus obtained for each patient, and for each substance.
• the cohort of patients (the number of patients on which the substance of interest is tested) to be at least 10 patients, preferably at least 20 or even 50 or 100 patients.
• the minimum number of subjects in the cohort of patients in order to obtain results which are statistically significant.
• the invention also relates to a method for classifying (determining) the nature of a congenital Long QT syndrome in a patient, comprising the steps of
• a. Obtaining ECG data from the patient; b. applying the ECG data to a machine learning classifier configured to detect variations in the ECG data indicative of the risk of torsade de pointes event,
• this method can be performed on a patient, which has been detected as having a congenital long QT syndrome, but for which the genetic analysis has not yet been performed.
• This method thus makes it possible to adapt a treatment very early before the genetic confirmation is obtained (as it can take a few months).
• LQTS (Long QT Syndrome) diagnosis have mutations in one of three major LQTS - susceptibility genes (KCNQ1 , KCNH2 or SCN5A) with mutations in KCNQ1 being responsible for about 35% of LQTS (type 1 , LQT1), KCNH2 mutations being approximately 30% LQTS (LQT2) and SCN5A mutations being about 10% of LQTS (LQT3).
• genes are minor LQTS genes.
• the machine-learning classifier system will be able to detect whether or not such patient has actually been classified as LQTS by analyzing the ECG of such patient.
• the system herein disclosed thus makes it possible to identify whether a given mutation classified as a variant of unknown significance is more likely to be pathogenic or benign.
• the invention thus relates to a method for determining whether a mutation of interest in a gene is susceptible to be associated with a congenital LQTS, comprising
• the output of the machine-learning classifier is a likelihood that the patient has prolonged QT (when the machine-learning classifier has been trained with ECGs from patients having received QT-prolonging drugs and from patients not having received QT-prolonging drugs), and that it is thus possible to classify the patients in one of these two classes; if the patient is classified in the class of patients having received QT-prolonging drugs, it is considered to have a prolonged QT. If the patient has a prolonged QT, if the mutation is thus susceptible to be associated, linked or causative to this condition. If the patient has not a prolonged QT, the mutation is likely not to be causative or associated to LQTS.
• the result will be more specific if the training of the neural network was performed with classes that are specific of congenital Long QT and of their type 1 , 2, 3 or other). However, as shown in the examples, the training with only data from patients with QT-prolonging drugs also made it possible to identify congenital long QT.
• the mutation of interest is in a gene as cited above (either one of the main three genes, or in a minor gene).
• beta-blockers class I or Class I la
• Atenolol, Nadolol, Propranolol can be used for LQTS (especially for LQT1 and LQT2)
• Mexiletine, Ranolazine or flecainide non-selective voltage-gated sodium channel blockers
• the invention also relates to a method for detecting (or estimating) the risk for a given patient to present an episode of torsade de pointes, comprising the steps of:
• obtaining an output from the machine learning classifier (in particular a neural network system), wherein the output provides a likelihood of the risk for the patient to have a torsade de pointe event.
• the ECG data is sent to a remote machine learning classifier by electronic/network means (such as cabled or wireless network, GSM, 3G or wifi) and the output is sent to the patient and/or a physician device (phone, computer) by electronic/network means (such as by email or by a Short Message on a phone device (SMS)).
• a physician device such as by email or by a Short Message on a phone device (SMS)
• SMS Short Message on a phone device
• it is sent to the physician so that the physician can thus determine the risk of TdP for the patient and act accordingly.
• such method is performed daily. It is preferred when communications between the machine measuring the ECG and the remote server containing the machine learning classifier is encrypted and between the remote server and the patient or physician device are encrypted.
• the likelihoods (probabilities or estimates) for a patient are calculated by averaging risk scores obtained when multiple ECG signals for the patient are applied to the machine learning classifier (voting system).
• the repetition of the machine learning classifier on multiple ECG samples is equivalent to a physician looking at different ECG samples to reach a conclusion.
• this method makes it possible to classify the patient in a given class, which is the one that is the most probable in view of the different likelihoods calculated by the machine learning classifier. It is possible to process multiple ECG from the same patient were processed by the models and to affect the patient in a class when the majority of the outputs indicate a higher probability of the patient of being in that class.
• the ECG signal can be acquired on a single lead (preferably the V3 or Dll lead). It is however preferred when the ECG signal is acquired from more than one lead, preferably from at least 3, more preferably from at least 6 or from at least 8 leads. Any lead can also be used individually. Dl, V4 or V6 leads are of particular interest.
• the inventors have determined in particular that using a machine-learning classifier, that has been configured (taught) with ECG coming from patients having been administered a QT-prolonging drug and with patients to whom no QT- prolonging drug has been administered, makes it possible to actually detect whether a torsade de pointe episode is to occur within 48 or 24 hours, or to detect whether a torsade de pointe episode will not occur within 48 or 24 hours. It is important to understand that the risk of having a torsade de pointes episode is not strictly correlated with a prolonged (or enhanced, or increased) QT. Although such prolonged QT increases the risk (in the sense that there is an increased relative risk in the population of patients with increased QT vs. a population of patient with normal QT), such increase by itself is not sufficiently predictive of the fact that a torsade de pointes will actually occur.
• the population risk (increased risk in the population) doesn’t provide by itself the information for a given patient in the population.
• the patient is generally a patient to whom is administered a drug that is known to increase the QT and hence to increase the risk of torsade de pointes events.
• the duty of the physician is to monitor the patient in order to decrease its individual risk. Monitoring may be by a constant hospital surveillance which is costly and occupies beds in the hospital. It would thus be preferable for the physician to be able to rule out the fact that the patient will indeed have a torsade de pointes episode in the near future (within 48 or 24 hours) and to only closely monitor the patients that are the most at risk.
• the inventors have shown that the training of the machine-learning system with a database as disclosed in the examples makes it possible to obtain an output that goes beyond the training, when new ECGs are provided, and that is able to determine a risk of torsades de pointes with high specificity.
• sensitivity is the probability that the diagnosis is positive in individuals having the condition (detection of true positives): the test is positive if the patient is having the condition.
• Positive predictive value is the probability of having the disease if the diagnostic test is positive (i.e. that the patient is not a false positive): the patient is having the condition if the test is positive.
• Negative predictive value is the probability of not having the condition if the diagnostic test is negative (that the patient is not a false negative): the patient is not having the condition if the test is negative.
• the prevalence of the condition is low.
• the NPV will be high. It is thus possible to say that a person has not any risk (ir a very little risk) of having a torsade de pointes event in the near future (within 48 or 24 hours) if it is not detected as such by the system.
• the inventors showed that the system is very good at detecting patients who will not have a torsade de pointes due to the high sensitivity, even though some patients may be detected as false positive due to a specificity that is not as high as the sensitivity. It is thus possible to reduce the number of patients to monitor, and focus on a subgroup for which there is a higher prevalence of TdP event (higher risk).
• the inventors showed that the test is semi-quantitative (i.e. the higher the score, the higher the risk). For patients above a given threshold, the individual risk will increase and one can increase the monitoring, stop the administration of the drug that is at risk (or decrease the dosage), and/or correct any possible hydroelectrolytic troubles by balancing the magnesium or potassium blood concentrations, maintaining or getting the patient in the hospital for surveillance.
• the machine-learning classifier (preferably a neural network) provides, as the output, a score.
• the inventors have shown that the higher the score, the higher the risk of a torsade de pointes event within 48 or 24 hours, and that it is highly specific. It is to be noted that the value of the score doesn’t fully correlate with the increase in the QT: hence, the classifier is able to classify the patients using other elements than merely the QT length.
• the information provided by the model (as the score) is thus different from the information delivered by the measure of the QT length.
• the invention also relates to a method for determining whether a subject is at risk (or at increased risk) of having an episode of torsade de pointes within 48 hours, comprising a. isolating a data segment from an ECG signal acquired from the subject and extracting such data segment
• the output provides an estimate of the risk of having a torsade de pointes event within 48 hours.
• the invention also relates to a method for determining whether a subject is at risk (or at increased risk) of having an episode of torsade de pointes within 24 hours, comprising
• the output provides an estimate of the risk of having a torsade de pointes event within 24 hours. As indicated, the risk is higher when the output is higher.
• the method may contain a step of comparing the output to a determined threshold and assessing the risk of having a TdP if the output is higher than the threshold.
• the inventors have trained the model so that the risk of torsades de pointes is higher when the score is higher. This is completely linked to the annotation of the data as provided to the machine learning classifier at the time of training. Using a different annotation could lead to another model in which the risk would be higher when the score is lower.
• the point is that the comparison is to be made with a threshold, which enables the classification of the patient as“at risk” or“not at risk” and that the threshold and the classification will depend on choices made for the specificity, sensitivity, and type of training of the system.
• the present disclosure shows that it is possible to train a machine learning classifier using ECG data from multiple or single leads
• Such trained models are able to discriminate/classify the patients having received a QT-prolonging drug from patients not having received such drug
• the models are also able to identify the presence or absence of the congenital LongQT conditions, and its nature
• the models are also able to predict the risk of occurrence of a TdP event within 24 or 48 hours, and/or to predict the non-occurrence of a TdP event within 24 or 48 hours.
• Figure 1 A. ROC curves indicating the separation between Sot+ subjects and (from left to right) controls, LQT-1 , LQT-3 and LQT-2 subjects. B. ROC curves indicating the separation between LQT-2 subjects and (from left to right) controls, LQT-1 , LQT-3 and Sot+ subjects.
• Figure 2 ROC curve indicating the separation between ECGs during TdP events and controls subjects.
• the first cohort contained electrocardiograms from 990 healthy subjects before and after the consumption of 80mg of Sotalol at different periods of time (namely at 2, 3, 4 and 5 hours after Sotalol intake). Each patient had a different number of recordings, as these recordings were either repeated or not at each time interval. Each subject had at least one recording at basal and T3. Sotalol concentration was measured in serum, at 2, 3 and 5 hours after intake, but this was not available for all subjects. Before the Sotalol intake, all signals were associated to a zero concentration of Sotalol. This first cohort was used to train the CNN (Convolutional Neural Network) and ResNet models that classify individuals in Sot+/Sot- groups and predict the concentration of Sotalol in the blood.
• CNN Convolutional Neural Network
• CNN convolutional neural networks
• the first (M1) included only ECG data before and after Sotalol intake (respectively denoted below as Control and Sot+), while the other (M2) also included basic clinical information, namely, age, sex and kalemia quantified at study baseline.
• Control and Sot+ included only ECG data before and after Sotalol intake
• M2 also included basic clinical information, namely, age, sex and kalemia quantified at study baseline.
• Different models M1 were developed. They all presented the same whole performance, although with some differences in assessment for some individual during the testing phase. Preparation of the ECG data
• Raw ECG was used, acquired with the Cardioplug device (Cardionics Inc). The ECGs were associated to 8 leads (I, II, V1 , V2, V3, V4, V5, V6). Each recording lasted 10s, with a sample size of 500 Hz.
• the data in the format of .SCP files were parsed using the Biosig library (http://biosig.sourceforge.net/index.html).
• the ECG data used corresponds to a segment of 10 consecutive seconds of a greater ECG acquisition.
• the data was entered as a 3 D tensor (8 leads, 5000 time points for each lead, Sot+ or Sot-).
• the data was entered as a 2D tensor (8 leads, 5000 time points for each lead).
• the classification was performed as a binary classification: no drug (Sot-) vs. drug (Sot+).
• the recordings were used associated to inclusion, basal and TO as Sot- and recordings after drug intake (at T1 , T2, T3, T4) as Sot+.
• Performances were computed using both single-signal analysis and by averaging risk scores from multiple recordings for a given patient and condition.
• the output provided by the models is a probability (likelihood) of being Sot+ (having ingested Sotalol).
• a voting strategy can be used by processing one ECG from a patient with multiple models (trained separately, using similar architectures or different), and affecting the patient to the Sot+ (resp Sot-) class if the majority of the models indicate a probability of being Sot+ (resp. Sot-) higher than 0.5 (50%). Other cutoffs could also be used.
• Sotalol intake is accurately detected from raw ECG signals
• the performance of the models was evaluated either at the single ECG level or averaged by patient for each of the 10 times 10-cross validation folds (CV). It was observed that the average test accuracy in CV is comparable to the holdout accuracy as is the overall empirical accuracy and the empirical in the training process (CV), indicating a good estimation of the performance of the models.
• Sotalol-intake classification models discriminate long-QT congenital profiles
• M1 the simplest (M1) model trained to discriminate Sotalol-intake patients was used to classify long-QT congenital patients from the LQT-congenital cohort.
• Sotalol-intake classification models identify patients with Torsade de Pointes at the time of the event
• ECG was performed on these patients and the ECG signals were applied to the neural network classifier as disclosed above.
• the likelihood of being at risk of torsades de pointes were 0.991629, 0.989505, and 0.072291 (thre ECG).
• Visit 2 0.998443, 0.999096, and 0.999811.
• QT prolongation has been shown to be associated to TdP and therefore many studies considered it as a TdP surrogate. However, recent studies demonstrated that QT prolongation alone is not a reliable predictor due to the high false positive and false negative rates (Hondeghem, 2018; see also AUC for discriminating Sot+ and contrils using QT alone).
• Another model is developed, using as the training set, ECG from controls, patients having ingested sotalol, LQT-1 , LQT-2 and LQT-3 patients.
• This model will provide, as the output the probability for the patient to be in each of the five classes.
• Using a voting system preferably by providing multiple ECG of the patient or alternatively by using multiple CNN models, it is possible to provide information for the physician and allows an appropriate care.
• Models can also be developed and enriched, using multiple TdP potentially inducing drugs to be able to discriminate the drug that has been taken by the patient.
• the risk score was higher within the 24 hours of the TdP events versus ECG from the same individual more than 24 hours apart from the event.
• the output is a score from 0 to 100. The higher the score, the higher the risk.
• Table 1 provides some of the specificity and of the sensitivity values for various thresholds.
• Sensitivity and specificity for detecting torsade de pointes episodes within 24h depending on the threshold for ECG of patients not having TdP at the time of the ECG.
• the choice of the threshold is to be made by the physician, depending on whether he wishes to have a high specificity of high sensitivity.
• the convolutional neural network (CNN) as disclosed above was trained using the ECG data from only one lead.
• Every trained system was then tested by inputing ECG data of the lead used to train it, from a testing set, as disclosed above.
• the classification of patients was as accurate as for the CNN trained using the ECG data from all leads (composite model) with AUROC ranging from 0.88 to 0.98.
• the models were also operative when the data obtained from one lead only was applied to a model that was trained on the basis of ECG data from another lead (or for all leads).
• the AUROC (representing the ability to discriminate patients having been administered a QT-prolonging drug from patients not having been administered such kind of drug) is 0.98.
• the AUROC is 0.94.
• the AUROC is 0.90.
• a V 4 ECG data is applied to a model trained with the data from the V5 lead, the AUROC 0.90.
• This data shows that a model trained with either an ECG from a single lead or with a composite ECG obtained from multiple leads can be used with any ECG data, whether from an individual lead or composite, with an AUROC mean (taking into account all combinations of the data from composite ECG or ECG from individual leads dl; dll, V1 , V2, V3, V4, V5 or V6, as the input or in the trained model) is 0.877 with standard deviation of 0.067.
• the machine-learning classifier can provide good analysis output when properly trained using only ECG data from only a single lead. This opens the way to application where the patient can himself place a single electrode to measure the ECG, which can be sent to a centralized server and analyzed by the machine classifier, so as to detect the risk for the patient to have a TdP and allow the physician to act if such risk is detected.
• the models trained with ECG data from a unique lead were also tested to determine their ability to discriminate LGT1 , LGT2 and LGT3 patients.
• the above data shows that it is possible to train a machine classifier with ECG data, either composite or from single leads, and that the trained machine classifier can
• Acharya U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information sciences, 405, 81-90. Acharya, U. R., Fujita, H., Oh, S. L, Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 415, 190-198. Acharya, U. R., Oh, S.
• arXiv preprint arXiv: 1707.01836.

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• 2019
  • 2019-06-05 EP EP19305730.4A patent/EP3748647A1/en not_active Ceased
• 2020
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