Methods and systems for myocardial infarction prediction

Abstract

Systems and methods are described for predicting the likelihood of myocardial infarction and / or accident, such as road traffic accident. Patient or user heart rate is detected and each of a plurality of heart rate n-variability (HRnV) and average heart rate n-variability (avHRnV) parameters are determined from the heart rate signal, from which a predictive model can predict the likelihood of myocardial infarction occurrence for the patient or user and, if necessary, alert the patient or user.

Description

Methods and systems for myocardial infarction predictionTechnical Field

[0001] The present invention relates, in general terms, to systems and methods for monitoring of cardiovascular health and conditions of a patient or user. In particular, the present invention relates to, but is not limited to, monitoring and prediction of myocardial infarction (Ml) or heart attack of a driver of a motor vehicle in real-time.Background

[0002] This background is provided for generally presenting the context of the disclosure. Contents of this background section are neither expressly nor implied admitted as prior art against the present disclosure.

[0003] Accidents, such as motor vehicle accidents, often referred to as car accidents or traffic collisions can result in property damage, injuries and even fatalities. Road safety and driver safety are key considerations when it comes to automotive industry and public safety. The same applies for clinical and domestic settings. Specifically, monitoring health-related conditions, especially acute cardiac events, of a patient is of great interest, since conditions, such as acute heart attacks, present a critical challenge to public safety on roads or can be difficult to detect early during an attack in a clinical or domestic setting. Traditionally, there are extremely limited means to monitor the health conditions of a patient accurately, timely, and pragmatically. However, with the rise of smart sensors and artificial intelligence, it is technically and economically viable to monitor health in real-time.

[0004] The current available technology systems and methods lack valid methods to detect or predict adverse health conditions from physiological data captured by real-time monitoring systems. There are currently no functional solutions for patient health monitoring. Current clinical solutions for Ml prediction require invasive tests and accurate electrocardiogram (ECG) recordings, renderingthem unsuitable for use in in automotives or expensive to provide in clinical and domestic settings.

[0005] It would be desirable to overcome or ameliorate at least one of the abovedescribed problems, or at least to provide a useful alternative.Summary

[0006] Disclosed is a system for predicting a likelihood of a road traffic accident, comprising: memory; at least one processor (processor(s)); and a prediction module comprising a predictive model trained to predict a likelihood of a road traffic accident, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to: receive a heart rate signal, comprising a plurality of heart beats, from a non- invasive heart rate sensor, the non-invasive heart rate sensor being position to detect a heart beat of a driver; determine a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; apply the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of road traffic accident due to myocardial infarction occurrence for the driver; and output a signal alerting the driver if the predicted likelihood exceeds a predetermined threshold.

[0007] Disclosed is a system for predicting a likelihood of myocardial infarction, comprising: memory; at least one processor (processor(s)); and a prediction module comprising a predictive model trained to predict a likelihood of myocardial infarction, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to: receive the heart rate signal, comprising a plurality of heart beats, from a non- invasive heart rate sensor, the non-invasive heart rate sensor being position to detect a heart beat of a patient; determine a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; apply the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of myocardial infarction occurrence for the patient; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

[0008] Disclosed is a method for predicting a likelihood of myocardial infarction, the method comprising:receiving a heart rate signal, detected by a non-invasive heart rate sensor, the signal comprising a plurality of heart beats of a patient; determining a value for each of a plurality of heart rate n-variability (HRnVn) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; applying the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of myocardial infarction occurrence for the patient, the predictive model being trained to predict a likelihood of myocardial infarction; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

[0009] Disclosed is a method for predicting a likelihood of road traffic accident, the method comprising: receiving a heart rate signal, detected by a non-invasive heart rate sensor, the signal comprising a plurality of heart beats of a patient; determining a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; applying the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of road traffic accident due to myocardial infarction occurrence for the patient; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

[0010] Disclosed is a system for predicting a likelihood of an accident, comprising: memory; at least one processor (processor(s)); and a prediction module comprising a predictive model trained to predict a likelihood of the accident, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to: receive a heart rate signal, comprising a plurality of heart beats, from a non- invasive heart rate sensor, the non-invasive heart rate sensor being position to detect a heart beat of a patient; determine a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; apply the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of the accident due to myocardial infarction occurrence for the patient; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.Brief description of the drawings

[0011] Some embodiments of systems and methods for myocardial infarction prediction, in accordance with the present disclosure, will now be described, by way of non-limiting example, with reference to the drawings in which:

[0012] Figure 1 illustrates a demonstration of the generation of RR3I2or avRR3I2from a RRI series;

[0013] Figure 2 illustrates an overview of variable preparation and selection process;

[0014] Figure 3 is a flow diagram of a method according to an embodiment of the invention;

[0015] Figure 4 illustrates receiver operating characteristic (ROC) curves of the models with and without avHRnV extension;

[0016] Figure 5 illustrates a system for implementing the methods of Figure 1 ; and

[0017] Figure 6 illustrates a method for training a predictive model used by the system of Figure 5 in predicting a likelihood of road traffic accident due to Ml, or a likelihood of Ml itself.Detailed description

[0018] Embodiments relate to systems and methods for monitoring of cardiovascular conditions, especially in predicting acute cardiac events such as myocardial infarction (Ml) among drivers of motor vehicles, with the intention to alert drivers if the likelihood of road traffic accident is high (i.e., above a predetermined threshold), due to a correspondingly high likelihood of Ml. Embodiments relate to systems and methods suitable for real-time implementation in automotives. The ability to predict such acute cardiac events among drivers in real-time by processing data obtained non-invasively while driving motor vehicles can allow for appropriate actions to be taken by drivers to pull over to a safe place, or get immediate medical attention, and consequently avoid motor vehicle accidents. The system and methods may operate in real-time, for practically immediate response to likely onset of Ml.

[0019] Hereinafter, the systems and methods for real-time prediction of the likelihood of road traffic accident, or Ml, for a driver of a motor vehicle will be described in detail with reference to Figure 1 to Figure 5 according to the preferred embodiments. It is to be understood that limiting the description to the preferred embodiments of the invention is merely to facilitate discussion of the present invention. Various modifications may be made, to the preferred embodiments, without departing from the scope of the appended claims.

[0020] Clinically, likelihood or risk of Ml is usually predicted using patients’ vital signs, medical history, blood tests, and other clinical tests. Heart rate variability (HRV) has been found to be useful in improving Ml prediction, and thus in predicting the likelihood of accidents resulting from Ml. HRV evaluates changes in cardiac autonomic regulation, and is closely related to the autonomic nervous system (ANS). HRV studies the variation of heartbeat intervals or R-to-R peak intervals (RRIs) and, in a clinical setting, would be derived from ECG signals. However, ECG machines are generally unsuitable for use in cars and other vehicles or automotives. For this reason, the heartbeat sensors referred to in the present disclosure are non- invasive sensors that acquire heartbeat signals through non-invasive means - e.g., a smartwatch with heart rate detector, or an infrared sensor. The heart rate sensors referred to herein may also therefore be contactless sensors - e.g., infrared detector that does not require skin contact. It is believed that a decrease of variations in HRV is usually correlated with morbidity and mortality. In a preferred embodiment, PhysioNet Cardiovascular Signal Toolbox is used for RRI preprocessing and conventional HRV metrics calculations.

[0021] Embodiments incorporate an artificial intelligence (Al) module, average heart rate n-variability (avHRnV) metrics to predict acute cardiac events, and thus, in some embodiments, to infer a likelihood of road traffic accident resulting from Ml. This Al module may also take into account heart rate n-variability (HRnV) metrics (to complement HRV analysis by considering HRV behaviors in varying scales), medical history, demographic information, patient preliminary physiological measurements and others. The physiological meanings and the processingmechanism of HRnV are not clear in every detail. One of the disadvantages of using HRnV is its use of summation windows, which increases some metrics in time and frequency domains as the parameters of HRnV increase, making the interpretation and comparison between different groups of HRnV metrics difficult. Herein, avHRnV is introduced as an extension of HRnV to address this issue and further improve the performance of HRnV with its application in Ml prediction for driver’s health monitoring.

[0022] In some embodiments, a list of variables or metrics is determined and input into a logistic regression algorithm to build the predictive model - e.g., a logistic model. The logistic regression algorithm may be a stepwise logistic regression algorithm, and / or may be multivariate. In other embodiments, the predictive model may apply kernel methods to HRnV and avHRnV metrics to capture the nonlinear interactions introduced by avHRnV and to improve the accuracy of the prediction model without the need of acquiring additional measurements from drivers.

[0023] The predictive model may adopt kernel functions (e.g. polynomial kernel and radial basis function kernel) to combine HRnV and avHRnV parameters in a nonlinear manner. The rationale for this algorithm is that some HRnV and avHRnV parameters are linearly linked, which could reduce the discriminatory power in predicting adverse clinical outcomes. With HRnV, avHRnV, and avHRnVm as inputs, various types of kernel function-mapped new nonlinear feature vectors can be created, e.g., k(HRnV, avHRnV), k(HRnV, avHRnVm), where (-) is the kernel function and HRnV, avHRnV and avHRnVm are vectors of identical dimensions. Notably, HRnV for n = 1 is HRV and, similarly, avHRnVm for n = m = 1 is HRV. Thus, with appropriate parametrization, the comparisons can include relationships across tuples involving HRV, HRnV, HRnVm, avHRnV and avHRnVm, though pairs will be used for illustration purposes.

[0024] Multiple kernel functions may be used such that multiple sets of converted feature vectors are available. When choosing kt as the kernel function, the parameters / metrics may include k;(HRnV, avHRnV), kt(HRnV, avHRnVm), where"t" is an integer of any value depending on the choice of kernel functions. Newly created kernel features may be integrated with basic patient information and clinical investigation results as inputs to feed into a prediction algorithm, such as logistic regression (particularly where stepwise logistic regression has been used for selection of variables in the model), neural network, support vector machine, decision tree, random forest, boosting, or ensemble learner etc.

[0025] Each HRnV or avHRnV analysis may be a conventional HRV analysis performed on the corresponding interval sequence (i.e., RRI or RRnlm), some embodiments group the metrics from each HRnV or avHRnV analysis as a metrics vector. For a single patient, multiple HRnV and multiple avHRnV analyses can be performed using the same source RRI measured from the patient, resulting in a number of metrics vectors (vector representations) of the same length (e.g., HRnV metrics vector, HR3V2metrics vector). Kernel methods can then be applied on these metrics vectors to reflect the nonlinear interactions between them.

[0026] Considering two different HRnV and / or avHRnV metrics vectors obtained from the same patient ( V(aV)HR .Vkand V(av)HRnVmwill be used for illustration purposes) of the same length, kernel methods involve calculating the inner product of the two vectors in a high dimensional vector space, measuring the nonlinear interactions between the vectors in their original space. The inner productfrom kernel ktis given by: tfkxnm= kt(yQav)HRjVk'V{av)HRnVm)

[0027] Notably, the present methods may use a pair of HRnV metrics, or a pair of avHRnV metrics, or may use both HRnV and avHRnV metrics.

[0028] Some embodiments use one or more than one of six different forms of kernel for kt. The six different forms include cosine similarity, polynomial kernel, sigmoid kernel, RBF kernel, Laplacian kernel, and Chi-squared kernel. The innerproducts obtained from these kernel methods can then be treated as candidates (inputs / parameters) for the stepwise model or the prediction model.

[0029] In some embodiments, HRnV and avHRnV analyses were performed on the source RRI from each of the patients with both HRnV and avHRnV parameters n and m up to 3, resulting in 6 different HRnV metrics vectors (including the conventional HRV metrics vector), and 5 avHRnV vectors. The nonlinear interactions were measured between every unique pair from the 11 metrics vectors, providing 55 inner products for each of the kernel methods. In total, 330 different inner products were added to each patient’s data.

[0030] Figure 6 provides a graphical illustration of various steps of kernel methods. At step 610, R-R interval signals (heart beat data or signals) is obtained from a non-invasive sensor. The received data is subjected to cleaning and signal processing operations at step 612, if necessary. These signal processing steps can be standard filtration and noise removal steps and others. Step 614, HRnVm(includes HRV and HRnV, where n=1 and / or m=1 ) and avHRnVm (includes avHRnVm, where m = 1 ) metrics are computed. At step 616, the HRnV and avHRnV metrics data is transformed into vectors and subjected to kernelization using any one or more of the method identified above. Examples of kernel metrics are illustrated in blocks 616(1 ) and 616(t). One or more kernel metrics may be computed based on various potential combinations of HRnV and avHRnV metrics. The kernel metrics may be combined with any driver (patient) data, clinical information (i.e., a training dataset comprising at least one of patient demographic data and clinical data relating to myocardial infarction) and other vital signs, symptoms and relevant information, and provided input to the prediction module at step 620 to determine a likelihood (also referred to as "risk") of road traffic accident due to Ml, or Ml itself, for the driver. If the Ml risk is above a predetermined threshold - e.g., where the predictive model outputs a likelihood of Ml between 0 and 1 , the predetermined threshold may be 0.2 or 0.5 or any other desired threshold which may, for example, be experimentally determined - the predicted likelihood is outputted at 622 in the form of an alert. The same threshold determination can be used to instead instructthe driver to pull to the side of the road, to a clinic, or other area where the car is safely located and, ideally, the driver's condition can be treated. The alert may be produced by pushing an alarm or notification to the driver's mobile device, automatically alerting a clinic or physician (e.g. by pushing a notification to the driver's mobile device, that then triggers an application on the mobile device to send the alert to a predetermined mobile device number or in the form of an email to the email address of an emergency department).

[0031] In a study of the present methods, a retrospective analysis was conducted of data collected from a sample of patients presented to the Emergency Department (ED) of Singapore General Hospital (SGH) from September 2010 to July 2015. SGH is the largest tertiary care hospital in Singapore, with an ED that sees between 300-500 patients daily. All patients recruited in the study were classified using the Patient Acuity Category Scale (PACS), with PACS 1 patients being the most critically ill and PACS 4 patients being non-urgent. The 795 patients finally included in this study were all classified to be PACS 1 or 2. The patients’ symptoms description, medical history (including cardiac disease history), vital signs taken at the ED, and RRI data derived from electrocardiogram (ECG) recorded at ED were included in this study. The decision to include these variables was made to mimic the data acquisition capability in an out-of-hospital setting, similar to the setting in automotives, where no blood test or ECG recording can be performed. Table 1 provides an exhaustive overview of the data included in this study. The primary endpoint for the prediction model was Ml within 30 days (30-day Ml) from the patient’s presentation.Table 1 -- Descriptions of dataThe HRnV Method and the avHRnV Extension

[0032] Embodiments comprise processing of the heartbeat signals of drivers obtained from infrared sensors or any other contactless sensors to convert the original heartbeat signals to R-R interval (RRI) sequences.

[0033] The HRnV method for alternative RRI representation for HRV utilizes sliding and stridden summation windows on the original RRI. The resulted intervals are denoted as RRnlmintervals, which can be fed into conventional HRV analysis as normal RRI to obtain HRnVmmetrics. For clarification, the term ‘HRnV’ refers to the name of the method (i.e., heart rate n-variability) and the metrics calculated using this method, while HRnVmrefer to the derived metrics based on the RRnImintervals with specified n and m.

[0034] The summation window used in the original HRnV generates RRnlmwith increasing amplitude as n increases, making comparison between different groups of HRnV metrics (e.g., comparison between HR2V]and HR3V±metrics) difficult. In a preferred embodiment, extension of HRnV is introduced to use averaging window instead of summation. The resulting intervals and metrics are denoted as avRRnImand avHRnVmrespectively.

[0035] The HRnV method and the avHRnV extension require specification of two parameters: the summation parameter n and the stride parameter m. Both n and mcan take any positive integer values (i.e., n,m >= 1) though m must be less than n or less than or equal to n (m <= n). To describe the RRnlmintervals generation used in HRnV and avHRnV, consider a series of clean input RRI as XL(i - 1,2,3, -,N) of length N. With specified parameters n and m, a new series of RRnlmor avRRnImintervals, Yt(i = 1,2,3, ..., M) of length M (M <= N) can be expressed as:

[0036] The weight of each data point, w, is set to 1 when using the original HRnV method and - when using the avHRnV method. The length of the new intervals, M, is given by M = [w~^+1] , where [ ■ J represents the floor function. When the parameters n and m are equal, the RRnlm(or R7?n / n)intervals can be abbreviated as RRnI. Consequently, metrics calculated based on RRnI can be abbreviated as HRnV. Essentially, the original RRI is equivalent to RRJ intervals, and HRV metrics are equivalent to HR^Y metrics. Figure 1 presents a demonstration of the generation of RR3I2or avRR3l2from a toy RRI series.

[0037] The RRnlmand avRRnlmintervals can be analyzed as normal RRIs using the conventional HRV analysis process. Metrics characterizing these intervals can be categorized in three different domains, and the detailed list of such metrics can be found in Table 2 as shown below. All the processing and metrics calculation of RRI are done using the HRnV-Calc software with modified functionalities for avHRnV. All HRnV and avHRnV metrics used in this study were calculated with all combinations of parameters n and m being not greater than 3.Table 2 -- Descriptions of HRV, HRnV, and avHRnV MetricsStatistical analysis

[0038] Figure 2 provides a comprehensive overview 200 of the variable preparation and selection process. All variables selected for this study as shown in Table 1 (202), including HRV, HRnV, and avHRnV metrics (208) calculated from the original RRI (206) acquired from patients (e.g., drivers), were first analyzed using univariate tests (204). Each variable was evaluated as an individual predictor of the primary outcome (30-day Ml). For categorical variables, the Chi-Square test was used, while the Mann-Whitney U-test was used for numerical variables. Only the variables with p-value no greater than 0.4 were selected for the subsequent analysis, where collinearity within the variables were eliminated based on Pearson’s correlation coefficients calculated for every unique pair of the selected variables (210). For a pair of variables with Pearson’s correlation coefficient being no less than 0.85, the variable with higher p-value was eliminated.

[0039] The final list of variables was then input into multivariate stepwise logistic regression with backward elimination (212) to build the predictive model. Backward elimination was chosen for the stepwise variable selection because it has the advantage to assess the joint predictive ability of variables, and it removes the least essential variables. The eliminated variables were not allowed to re-enter the model in some embodiments. In other embodiments, every combination of variables was examined. Examination of every combination of variables required significant computing resources with the risk of overfitting the model when the number of variables is large. The goal for the prediction model was Ml within 30 days from the patient’s ordriver's presentation (214). Thus, the training dataset was extracted from past data of a pool of patients, and the predictive model was trained to identify patterns in the values corresponding to myocardial infarction occurrence reflected in the past data.

[0040] Figure 3 illustrates a flow diagram of a method for predicting myocardial infarction according to an embodiment of the invention. The method comprises receiving a heart rate signal from a heart rate sensor. The signal includes a pluralityof heart beats from a user (step 302). The user may be a person in a clinical setting, domestic setting and any other setting include driving as set out in relation to other examples herein. A value for each of a plurality of heart rate variability (HRV) parameters is then determined, which can be or include a plurality of HRnV parameters (step 304) and a plurality of avHRnV parameters (step 306). Steps 304 and 306 can occur concurrently, since all relevant metrics can be derived from the original heart beat signal. The predictive model (e.g., a model trained as set out above, which may be a logistic regression model) is then applied to the values to predict a likelihood of myocardial infarction occurrence for the user (step 308). The likelihood, if above a predetermined threshold, will then be outputted in the form of an alarm or alert (step 310). Step 310, in some embodiments, can be replaced with outputting an alert to the user, clinic or emergency services (e.g., road traffic authority, ambulance service, paramedics and others), advising the user to move to a safe location. In these embodiments, determining the likelihood of Ml may be sufficient to then infer that an accident is likely and thus trigger the alert. In this embodiment, an “accident” can include any accident include myocardial infarction while in the home, clinic or other setting. Similarly, other embodiments described with reference to drivers of motor vehicles and “road traffic accidents” may similarly be applied to other settings and other types of “accident” as dictated or permitted by context. In some embodiments, the term "patient" may be used interchangeably with “user”, “subject” and other terms, and refers to human individual who is receiving, has received, or is intended to receive medical treatment, diagnosis, or care. Unless otherwise specified, the term may include individual at risk of developing a condition, those suspected of having a condition, or those undergoing preventive, diagnostic, therapeutic, or palliative care. In some embodiments, the term may also include healthy subjects that are not having a condition.Results

[0041] To examine the usefulness of adding avHRnV metrics to the HRnV metrics family, two groups of variables are prepared: with and without avHRnVmetrics. The group with avHRnV metrics contained 365 variables with 199 of them having p-values no more than 0.4. After collinearity elimination, 78 variables were kept for stepwise regression, which selected 21 variables for the final predictive model. The groups without avHRnV metrics contained 210 variables with 125 of them having p-values no more than 0.4. 68 variables were kept after collinearity elimination, and 21 of them were selected for the final model. Table 3 contains the full lists of variables selected in the predictive models for both groups. The two predictive models were compared using 10-fold cross-validation.

[0042] Figure 4 shows the ROC curves of the two models. The average area under the curve (AUG) performance of the model with avHRnV metrics is 0.770, while the average performance of the model without avHRnV is 0.739.Table 3 -- Final variable lists for the predictive models and corresponding odds ratios (OR) and p-values

[0043] Figure 5 illustrates a system for implementing the method 300. The system 500 comprises a heart rate sensor 510, for detecting the heart rate from a user (including a patient, driver or other user) and producing a signal 520 based on the heart rate. The signal is received by a system 530 for predicting a likelihood of Ml of the user from whom the signal was acquired - in some embodiments, the system 530 may include the sensor 510. The signal is processed by the processor(s) 532, using program code 538 stored in memory 534, to determine a plurality of values corresponding to HRrV and avHRnV parameters. A predictive model 536, also stored in memory 534, processes the values to determine a likelihood of Ml of the user based on the heart rate signal, or of an accident, such as a road traffic accident, resulting from Ml. If the likelihood of Ml exceeds a predetermined threshold, an alert is outputted in the form of an alarm as discussed above, by alert system 540.

[0044] It will be appreciated that many further modifications and permutations of various aspects of the described embodiments are possible. Accordingly, the described aspects are intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims.

[0045] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0046] Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0047] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Claims

Claims1 . A system for predicting a likelihood of a road traffic accident, comprising: memory; at least one processor (processor(s)); and a prediction module comprising a predictive model trained to predict a likelihood of a road traffic accident, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to: receive a heart rate signal, comprising a plurality of heart beats, from a non- invasive heart rate sensor, the non-invasive heart rate sensor being position to detect a heart beat of a driver; determine a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m. where n and m are natural numbers and m < n; apply the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of road traffic accident due to myocardial infarction occurrence for the driver; and output a signal alerting the driver if the predicted likelihood exceeds a predetermined threshold.

2. A system for predicting a likelihood of myocardial infarction, comprising: memory; at least one processor (processor(s)); anda prediction module comprising a predictive model trained to predict a likelihood of myocardial infarction, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to: receive the heart rate signal, comprising a plurality of heart beats, from a non- invasive heart rate sensor, the non-invasive heart rate sensor being position to detect a heart beat of a patient; determine a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; apply the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of myocardial infarction occurrence for the patient; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

3. The system of claim 1 or 2, wherein the predictive model is trained to predict the likelihood from a training dataset comprising at least one of patient demographic data and clinical data relating to myocardial infarction.

4. The system of claim 3, wherein the processor(s) extract the training dataset from past data of a pool of patients, and trains the predictive model to identify patterns in the values corresponding to myocardial infarction occurrence reflected in the past data.

5. The system of any one of claims 1 to 4, wherein the heart rate sensor is contactless.

6. The system of any one of claims 1 to 5, wherein the heart rate sensor is configured for use in a motor vehicle to obtain the heart rate signal.

7. A method for predicting a likelihood of myocardial infarction, the method comprising: receiving a heart rate signal, detected by a non-invasive heart rate sensor, the signal comprising a plurality of heart beats of a patient; determining a value for each of a plurality of heart rate n-variability (HRnVn) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; applying the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of myocardial infarction occurrence for the patient, the predictive model being trained to predict a likelihood of myocardial infarction; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

8. A method for predicting a likelihood of road traffic accident, the method comprising: receiving a heart rate signal, detected by a non-invasive heart rate sensor, the signal comprising a plurality of heart beats of a patient; determining a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n;applying the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of road traffic accident due to myocardial infarction occurrence for the patient; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

9. The method of claim 7 or 8, further comprising training the predictive model to predict the likelihood, using a training dataset comprising at least one of patient demographic data and clinical data relating to myocardial infarction.

10. The method of claim 9, further comprising extracting the training dataset from past data of a pool of patients, and training the predictive model to identify patterns in the values corresponding to myocardial infarction occurrence reflected in the past data.11 . The method of any one of claims 7 to 10, wherein the heart rate sensor is a contactless sensor.

12. The method of any one of claims 7 to 11 , wherein the heart rate sensor is configured for use in a motor vehicle to obtain the heart rate signal.

13. A system for predicting a likelihood of an accident, comprising: memory; at least one processor (processor(s)); and a prediction module comprising a predictive model trained to predict a likelihood of the accident, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to:receive a heart rate signal, comprising a plurality of heart beats, from a non- invasive heart rate sensor, the non-invasive heart rate sensor being position to detect a heart beat of a patient; determine a value for each of a plurality of heart rate n-variability (HRnVm) and average heart rate n-variability (avHRnVm) parameters from the heart rate signal, for a plurality of values of at least one of n and m, where n and m are natural numbers and m < n; apply the predictive model to the values of the HRnVm and avHRnVm parameters, to predict a likelihood of the accident due to myocardial infarction occurrence for the patient; and output a signal alerting the patient if the predicted likelihood exceeds a predetermined threshold.

14. The system of claim 13, wherein the predictive model is trained to predict the likelihood from a training dataset comprising at least one of patient demographic data and clinical data relating to myocardial infarction.

15. The system of claim 14, wherein the processor(s) extract the training dataset from past data of a pool of patients, and trains the predictive model to identify patterns in the values corresponding to myocardial infarction occurrence reflected in the past data.

16. The system of any one of claims 13 to 15, wherein the heart rate sensor is contactless.

Images