1. Introduction
Sudden cardiac arrest (SCA) is the sudden unexpected loss of heart function less than 1 h from the onset of symptoms [
1,
2]. SCA arises when a triggering factor that is either acquired or genetically-determined affects an anatomical or physiological substrate, manifesting into an abnormal heart rhythm such as ventricular tachycardia, which ultimately degenerates into the fatal ventricular fibrillation (VF) rhythm [
3].
SCA can occur at any age in a patient with or without a detectable heart disease [
4]. Approximately 15,000 Australians per year experience SCA and only 6–13% of patients survive more than one year after the event [
5]. SCA is one of the major causes of cardiovascular mortality and is a major public health issue both nationally and globally, with the annual cost of SCA amounting to approximately
$33 billion USD [
6]. This economic and health burden of SCA poses to society can be reduced with the improvement of patient outcomes through better detection systems [
7].
However, the main challenge associated with SCA detection lies in the fact that SCA can manifest in the complete absence of symptoms. Various methods have been investigated for the detection of SCA, each demonstrating strengths in various patient populations. Cardiac imaging modalities such as cardiac magnetic resonance imaging, echocardiography and cardiac positron emission tomography were found to be useful in patients with a suspected structural heart disease [
8]. However, there are many cases in which SCA may arise as a result of a non-cardiac cause. Electrophysiology study had also been investigated as a potential detection method, however, there is limited data on its prognostic value for SCA and it is limited due to its poor negative predictive value and its decrease in sensitivity with polymorphic ventricular tachycardia rhythms [
9]. The current most commonly explored detection method for SCA is electrocardiography (ECG). Early studies had already established the clinical importance of ECG detection, as 95% of SCA patients were found to have abnormal ECG readings [
10]. These early studies have highlighted the usefulness of ECG monitoring for SCA detection as it can aid in understanding the changes preceding the VF rhythm and have identified potential markers that may trigger SCA. The ECG markers that have been currently explored include: QTc interval, QRS duration, R-R interval, ST segment elevation, T-wave amplitude, and T-peak-to-T-end [
11,
12,
13,
14]. Though its efficacy as a detection method has been well established, the field is still developing and future large cohort studies on specific patient populations still need to be conducted. Overall, although a majority of both invasive and non-invasive tests have been employed and evaluated, there is currently no optimal detection method nor criteria specific for SCA [
15].
Currently, computer algorithms have been developed and utilized in the clinical setting for the quick detection of critical events such as ventricular fibrillation, in the case of SCA. These pattern recognition methods aim to either assign an individual heartbeat to a specific class or to detect the underlying pathology using information obtained from measurable features of the ECG, such as the R-R interval duration. The assumption is that features of individuals in the same class have similar values and, thus, occupy a specific region in the multidimensional feature space separated from other classes [
16]. Early studies on pattern recognition algorithms utilized linear classification methods, such as linear discriminant analysis, which assumed that biological signals were linear in nature. Although these methods provided reasonable results, the underlying non-linear features of these signals were ignored. As a majority of biological signals are non-linear in nature, this led to the need for further classification and, thus, research interest had shifted to non-linear classification methods, such as support vector machines [
17]. However, limitations lie in the current literature as time-frames of only up to 5 min prior to the onset of SCA have been thoroughly investigated, which, when applied to the clinical setting, does not provide enough time for a patient to respond with sufficient time to reach the hospital. The overall survival rate decreases by 10% each minute the patient remains in VF, thus, high importance is placed on detection as early identification and management is critical.
Since the early detection of SCA is highly important yet not well established, the aim of this study is to, first, statistically compare the importance of various ECG features, such as heart rate and R-R interval related markers, between normal and patients at risk of SCA and, secondly, and most importantly, to determine the ideal time-frame spanning up to three hours prior to the onset of VF in which electrocardiographic changes alluding to SCA can be detected and accurately classified.
4. Discussion
As the early detection of SCA is critical, yet not well established, the overall intention of this study was to determine the earliest time-frame prior to the onset of SCA in which ECG changes pertaining to SCA can be detected. Statistical comparison of the importance of each feature in comparison to a normal patient population, with a focus on time-based detection was also pertinent to this study.
Heart Rate. The first feature investigated in this study was mean resting heart rate, where patients in the SCA cohort exhibited a higher mean resting heart rate compared to the normal patient cohort. This correlated with literature findings, however, a larger difference between the SCA and normal cohort was observed in this study compared to previously conducted studies (18.9 vs. 7.5 bpm increase in the SCA cohort) [
22]. The mean heart rate was then found to gradually increase and decrease between 3 h prior to SCA onset before exhibiting a sudden drop during SCA onset. There was a statistically significant difference in the mean resting heart rate observed between the two cohorts at 3 h and 6 min prior to SCA onset. However, after further statistical analysis statistical significance was only achieved at 3 h prior to SCA onset and as the
p-value obtained was close to the cutoff value of 0.05, the findings should be considered as only marginally significant.
The mean resting heart rate is an important marker as it both contributes to and reflects cardiovascular pathophysiology [
23]. Heart rate is controlled by neural influences, where under normal physiological conditions the resting heart is under parasympathetic control, as proven in studies involving the pharmacological blockade of the autonomic influences with atropine [
24]. The initial increase in heart rate observed occurs as a result of the patient’s underlying cardiovascular disease, where there is a chronic imbalance between the sympathetic and parasympathetic control of the heart. The late decrease observed just prior to SCA onset may be attributed to two mechanisms. The first suggested mechanism is vagal withdrawal, which occurs as a result of contraction-perfusion mismatch and, consequently missed baroreflex input. A high heart rate results in a decrease in the diastolic perfusion time resulting in hemodynamically-insufficient ventricular contractions [
25]. Overall, it leads to deleterious effects on the overall cardiac output and ultimately results in hemodynamic collapse and the functional deterioration of the heart. The second suggested mechanism is due to premature ventricular contraction-mediated disturbance of the cardiac cycle. The increased sympathetic output due to CVD results in the heightened electrical instability of the heart. This in turn gives rise of premature ventricular contractions (PVC), which are early depolarizations originating from the ventricle [
26]. PVCs result in the delayed activation of the ventricular myocardium as depolarizations from the sinoatrial node are unable to reach the ventricles. As a result, there is a full compensatory pause before the next successive heartbeat following a PVC which, in turn, may contribute to the late decrease in heart rate [
27].
It has been widely reported across various large cohort studies, such as the Framingham heart study, that a higher mean resting heart rate is associated with an increase in cardiovascular mortality. Studies have exhibited that an increase in SCA risk was observed in heart rates exceeding 75 bpm [
14,
22,
27]. However, the mechanisms underlying the relationship between elevated resting heart rate and SCA risk are still poorly understood and remain to be elucidated.
Mean R-R Interval Duration. In a similar fashion to resting heart rate, the mean R-R interval duration was longer in SCA patients compared to normal patients. This observation aligns with early studies conducted on ambulatory ECGs of SCA patients, where long R-R interval cycles ranging between 640–1110 ms were observed in approximately 90% of patients prior to SCA [
14,
28]. These studies have suggested that this prolongation in the R-R interval observed during SCA occurs as a result of PVCs. As stated in the preceding section, PVCs are followed by a full compensatory pause as the depolarizations from the sinoatrial node fail to reach the ventricles [
14]. This, consequently, results in disturbances in the cardiac cycle, characterized by the shortening followed by the lengthening of the R-R cycle intervals [
29]. These early studies have also exhibited that the frequency of PVCs increase significantly from 1-h prior to SCA until the onset of SCA, which may explain the statistically significant differences in the mean R-R interval duration observed between a single time-frame prior to SCA and SCA onset observed in 50% of the SCA cohort [
14,
28]. It is also important to consider that external factors, such as the patient’s underlying etiology and medication, may also contribute to R-R interval prolongation.
Heart Rate Variability (RMSSD and SDSD). The final two features investigated in this study were two time-domain measures of heart rate variability (HRV); the square root of the mean of differences between adjacent R-R intervals (RMSSD) and the standard deviation of the differences between adjacent R-R intervals (SDSD). Studies have shown that beat-to-beat changes in the R-R interval may accurately reflect any variability in sinoatrial node activity and features that utilize the difference between adjacent R-R intervals are ideal for long term measurements of HRV [
30]. Both the RMSSD and the SDSD of the SCA cohort obtained in this study were higher compared to the normal cohort, although not to a statistically significant extent. Since these features utilize the R-R interval, the difference observed between the SCA and normal patient cohort may be attributed to the mechanisms related to the increase in PVC frequency, as previously discussed in the preceding section. However, the values obtained in this study do not correlate with literature findings which found lower HRV indices in SCA patients [
19]. Other literature studies have associated low HRV values as a marker of cardiac dysfunction, while high HRV on the other hand is associated with efficient cardiac autonomic mechanisms [
31,
32]. Thus, a high HRV index while the patient is at rest is deemed favorable as it indicates that the body is either able to tolerate stress or is strongly recovering from prior accumulated stress [
32]. However, it is important to note that a majority of patients in the SCA cohort possess underlying cardiovascular conditions and, as the patients in this study are older than the normal cohort, their hearts have been exposed to stress for a period of time. As a result, their heart may have already acclimatized to these stressful conditions, thus resulting in the high HRV values.
Statistical Classification and Machine Learning. The data was then subjected to statistical classification with the use of various classifiers. The first classifier tested was the linear classifier, which utilized linear discriminant analysis. The best accuracy was yielded at 8 min prior to SCA onset and was found to correlate with literature findings [
33,
34]. However, the values obtained in this study were slightly lower than the values obtained in similarly-conducted studies (72.80% vs. 74.36%) [
33]. This may be attributed to the signal quality, as the accuracy of a classifier depends on two factors; signal quality and the extracted features [
35]. SVM classification was then conducted on the dataset and there was an improvement in the accuracy values obtained for both the linear and non-linear SVM classifiers, both of which yielding higher accuracy rates compared to the linear classifier (72.8% vs. 78.9% both obtained at the 8 min prior time-frame). The values obtained from the non-linear SVM classifier were found to correlate with findings obtained from a similar study conducted using the same two ECG database, although the findings obtained for accuracy (83.9% vs. 88.0%), sensitivity (91.5% vs. 92.0%), and specificity (82.5% vs. 84.0%) were slightly lower compared to the literature [
34]. This slight variation in values may be due to interpatient variation, as in the literature only five patients each were used for the normal and SCA cohort, whereas the cohort numbers in this study were almost double for both the normal and SCA population.
As time starts to approach the onset of SCA, the overall risk of SCA increases and, thus, the percentage at which the classifier will correctly classify a patient at risk of SCA would consequently increase [
19]. Overall, the experimental results exhibited an increase in accuracy with each time window approaching SCA onset, particularly in the findings obtained from the non-linear SVM classifier.
The performance of each classifier based on the isolated time-frames were then quantitatively evaluated by calculating the area under the receiver operating characteristic (ROC) curve. The higher the AUC, the better the classifier performance [
36]. The highest overall AUC was obtained by the non-linear SVM classifier in the 2-min time-frame which exhibited an AUC of 0.88. However, there is an approximately 0.02 difference observed between the early time-frames (50 and 40 min prior) and the later time-frames (8 and 2 min prior), suggesting that the risk of SCA can be detected at an earlier time-frame with reasonable accuracy (See
Table 3). Overall, the non-linear SVM had the best performance across all time-frames compared to its linear counterparts, which may be due to the fact that it takes into account the non-linearity of ECG signals.
Across all three classifiers, the highest overall accuracy was obtained at 2 min prior to SCA onset. This is a time-frame in which a significant difference between the ECG recordings obtained from a patient at risk of SCA differing from a normal patient would be expected. This is due to the increase in the frequency of PVCs over time. Thus, more frequent PVCs at 2 min prior would result in larger compensatory pauses which, in turn, affects the HRV features of patients at risk of SCA, resulting in higher accuracies in classifying SCA and normal patients. A similar conclusion was made across various similarly conducted studies which also discovered that the 2-min time window provided the most useful information for SCA detection [
19,
33,
34,
35]. However, it is important to note that the overall aim of SCA detection is to identify patients at risk as early as possible in order to prevent serious repercussions, such as irreversible neurological damage, and even death. Thus, 2 min prior to the onset of SCA is not enough time to allow a patient to respond and seek management.
However, it was also observed that reasonable accuracy rates of 70–80% can be obtained across all three classifiers as early as 50 min prior to the onset of SCA. This, in turn, may provide sufficient time for a patient to respond and reach the hospital to immediately undergo SCA management, in turn reducing the risk of serious complications such as neurological damage, organ failure and, most importantly, death. These time-frames, however, are yet to be explored in the current literature as time-frames spanning only up to 5 min prior to SCA onset have been explored, which does not provide sufficient time for optimal patient response.