Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

BACKGROUND

Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.

OBJECTIVE

In this study, we aimed to evaluate the efficiency of discrete wavelet transform (DWT) based features extracted from HRV which were further selected by genetic algorithm (GA), and were deployed by support vector machine to HRV classification.

MATERIALS AND METHODS

In this paper, 53 ECGs including 3 different beat types (ventricular fibrillation (VF), atrial fibrillation (AF) and also normal sinus rhythm (NSR)), were selected from the MIT/BIH arrhythmia database. The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β), best features selection by GA and classification of normal and abnormal ECGs using the selected features by support vector machine (SVM).

RESULTS

The performance of the classification procedure employing the combination of selected features were evaluated using several measures including accuracy, sensitivity, specificity and precision which resulted in 97.14%, 97.54%, 96.9% and 97.64%, respectively.

CONCLUSION

A comparative analysis with the related existing methods illustrates the proposed method has a higher potential in the classification of AF and VF. The attempt to classify the ECG signal has been successfully achieved. The proposed method has shown a promising sensitivity of 97.54% which indicates that this technique is an excellent model for computer-aided diagnosis of cardiac arrhythmias.

Effects of mechanical-bed massage on exercise-induced back fatigue in athletes

[Purpose] The study aimed to preliminarily investigate the effects of mechanical-bed massage on exercise-induced back fatigue in athletes. [Subjects and Methods] Twelve male college athletes, randomly allocated to experimental or control groups, were instructed to perform reverse sit-up for 8 sessions until they became fatigued. The experimental group received a 20-min mechanical-bed massage session, while the control group rested on a bed for the same period of time. Visual Analogue Scale (VAS) on perceived back muscle fatigue, back muscle endurance, and Heart Rate Variability (HRV) parameters including stress index (SI), HRV index, SDNN, RMSSD, pNN50, LF, HF, and LF/HF were analyzed. [Results] Immediately and 24 hours after the intervention, the VAS significantly differed between the groups. Experimental group's HF was significantly higher immediately after the intervention than control group. Experimental group's LF and LF/HF were significantly lower immediately after the intervention than the control group. [Conclusion] Mechanical bed massage may help athletes overcome the subjective feelings of exercise-induced fatigue, modulate the automatic nervous system activity, especially for balancing sympathetic and parasympathetic activities. Therefore, mechanical bed massage may facilitate recovery from muscle and central fatigue after athlete training or competition.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training).

Multisubject "Learning" for Mental Workload Classification Using Concurrent EEG, fNIRS, and Physiological Measures

An accurate measure of mental workload level has diverse neuroergonomic applications ranging from brain computer interfacing to improving the efficiency of human operators. In this study, we integrated electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), and physiological measures for the classification of three workload levels in an n-back working memory task. A significantly better than chance level classification was achieved by EEG-alone, fNIRS-alone, physiological alone, and EEG+fNIRS based approaches. The results confirmed our previous finding that integrating EEG and fNIRS significantly improved workload classification compared to using EEG-alone or fNIRS-alone. The inclusion of physiological measures, however, does not significantly improves EEG-based or fNIRS-based workload classification. A major limitation of currently available mental workload assessment approaches is the requirement to record lengthy calibration data from the target subject to train workload classifiers. We show that by learning from the data of other subjects, workload classification accuracy can be improved especially when the amount of data from the target subject is small.

Inducing unconscious stress: Cardiovascular activity in response to subliminal presentation of threatening and neutral words

Stress-related cognitive processes may occur outside of awareness, here referred to as unconscious stress, and affect one's physiological state. Evidence supporting this idea would provide necessary clarification of the relationship between psychological stress and cardiovascular (CV) health problems. We tested the hypothesis that increases in mean arterial pressure (MAP) and total peripheral resistance (TPR) and decreases in heart rate variability (HRV) would be larger when threatening stimuli are presented outside of awareness, or subliminally, compared with neutral stimuli. Additionally, it was expected that trait worry and resting HRV, as common risk factors for CV disease, would moderate the effect. We presented a subliminal semantic priming paradigm to college students that were randomly assigned to the threat (n = 56) or neutral condition (n = 60) and assessed changes from baseline of MAP, TPR, and HRV. Level of trait worry was assessed with the Penn State Worry Questionnaire. The findings indicate that CV activity changed according to the hypothesized pattern: A higher MAP and TPR and a lower HRV in the threat condition compared with the neutral condition were found with practically meaningful effect sizes. However, these findings were only statistically significant for TPR. Furthermore, changes in CV activity were not moderated by trait worry or resting HRV. This is the first study to explicitly address the role of subliminally presented threat words on health-relevant outcome measures and suggests that unconscious stress can influence peripheral vascular resistance.

Lucky Rhythms in Orbitofrontal Cortex Bias Gambling Decisions in Humans

It is well established that emotions influence our decisions, yet the neural basis of this biasing effect is not well understood. Here we directly recorded local field potentials from the OrbitoFrontal Cortex (OFC) in five human subjects performing a financial decision-making task. We observed a striking increase in gamma-band (36-50 Hz) oscillatory activity that reflected subjects' decisions to make riskier choices. Additionally, these gamma rhythms were linked back to mismatched expectations or "luck" occurring in past trials. Specifically, when a subject expected to win but lost, the trial was defined as "unlucky" and when the subject expected to lose but won, the trial was defined as "lucky". Finally, a fading memory model of luck correlated to an objective measure of emotion, heart rate variability. Our findings suggest OFC may play a pivotal role in processing a subject's internal (emotional) state during financial decision-making, a particularly interesting result in light of the more recent "cognitive map" theory of OFC function.