Cardiovascular disease the major challenges in the current 21st century in terms of health care and related to diagnostic developments. In this pandemic COVID-19 scenario, the cardiovascular disease or non-cardiovascular disease has been increased like cardiac arrest or silent heart attack. According to WHO has guidelines, it is set to reduce 25% overall mortality rate due to cardiovascular disease upto 2025 on the priority basis kept as prevention and control. Some techniques developed for heart rate estimation from multimodal physiological signals namely ECG, AB, and PPG, EEG, EMG and EOG etc. are the part of cardiovascular and non-cardiovascular signals have been reviewed.
Human body consists of various major parts and heart [
When a person report for treatment or surgery then if certain designed equipments are used inside
the human body or ingested in the form of pills are well known as in-the-person category. If some
electrodes or sensors are used over the human body or touch the outer skin for ECG measurements is
well known to be on-the-person category and majorly used devices for ECG monitoring. In case of
off-the-person category it is found that minimal skin contact or ECG without skin contact. It is based
on the capacitive devices which measures the electric field changes induced by body allow ECG
measurement at distances of 1cm or more even with the help of sensors[
Various tests are used to diagnose heart disease[
Invasive Test: It is a medical practice of cut skin and insert into the body opening for test using instruments while doing such types of test [10, 11] biopsy, endoscopy, and cryotherapy etc is required.
Non-Invasive test: Non-invasive disease does not damage other organs of human body [
Cardiac Catheterization: common procedure that helps to diagnose heart disease [7,8] it can also be used to treat heart disease by opening blocked arteries with balloon angioplasty and stent placement.
These signals can be classified through the classifier [16,18] as SVM-support vector machine. Multimodal physiological [10-12] signals are helpful to monitor continuously the health of patients either in ICU or at home at night. In high-tech hospitals [9, 11] there are the availability of intelligent bed system [11-13] for the patients whom automatically health monitors[12,14] and send the signal to the cloud server. Mainly focused signals are Electrocardiogram (ECG), Arterial Blood Pressure (ABP), Electromyogram (EMG), Electroencephalogram (EEG) and Photoplethysmogram (PPG)[11,12,15] etc. The signals directly helpful to monitor the heartbeat are related to cardiovascular and these are ECG, ABP and PPG etc and those physiological signals are indirectly connected or helpful are called non-cardiovascular signals such as EEG,EMG and EOG etc.
In Figure 3, it represents the heart signal observed as ECG signal and shows some up-down lines like half sinewave and sharp peak like sawtooth waveform as monitored in electrons analog signals. Basically these have been termed as the English alphabets namely P; Q; R; S; T; U. These are the heart monitored ECG signals and termed as P-wave, T-wave, U-wave, and QRS complex wave. If we go through all the waves shows unique patterns. So these have defined as P-wave as atrial depolarization, T-wave as ventricular repolarization, U-wave as Papillary muscle repolarization and the last one is QRS complex wave as ventricular depolarization.
Eduardo Jose da S. Luz, et al [
Tian Zhou, et al [
Wei Wei, et al, [
Luis J.Mena et.al [
Deger Ayata, et al [8], proposed a novel emotion recognition algorithm from multimodal physiological signals for emotions aware healthcare systems. The physiological signals are collected from respiratory belt RB, photoplethysmography PPG, and fingertip temperature FTT sensors. The collection of the signal is done by wearable technologies. The results shows improved accuracy for arousal (69.86 to 73.08%) and from (69.53 to 72.18%) for valence. This is due to the multiple sources of physiological signals and their fusion increases the accuracy rate of emotion recognition.
Xiaowei Zhang, et al [9], proposed a regularized deep fusion of kernel machine framework for emotion recognition based on multimodal physiological signals. Experimental study shows two benchmark datasets which improve the subjects-independent emotion recognition and compared to single-modal classifiers or other fusion methods.
Chen Wang et al [10], proposed various methodologies to monitor or detection of heart rate (HR) using human face recording. This is done as there is motion in the cardiovascular activities or subtle color changes as the method used.
Syem Ishaque et al [11], restricted the physiological signals ECG, EDA (Electrodermal Activity), PPG (photoplethysmography), and respiration (RESP) analyzed the heart rate variability between each heartbeat w.r.t time. Various analysis of various research work have been analyzed HRV associated with morbidity and stress. Detection of HRV in motion is far from perfect situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. This can be improved with the advancement of machine learning techniques.
Fanny Larradet et al [12], focused on the methodological issues in real life data collection in the wild based on physiological signals like emotion recognition. In the paper common technique used to induce emotions for the physiological dataset creation to compare with existing dataset of real-life applications. Author also proposed a set of categories visual tool called Graphical assessment of reallife application-focused emotional dataset (GARAFED) and compared with existing physiological datasets for EMSR in the wild.
Quentin Meteier et al [13], discussed about the workload and task of drivers requested to regain the control of the car while performing the secondary task. In future, no longer of primary task for the driver as the automation of cars introduced. Measuring drivers workload continuously essential to support the driver and hence to limit the no. of accidents in takeover situations. For this purpose machine learning techniques is used to evaluate and classify the workload in real-time.
Haoran Xu et al [17], prepared the system for the hospital general ward to monitor wirelessly (wearable and artificial intelligence) using physiological signals (ECG, RESP and transmit data wireless). The system consists of multi-sensor wearable device, database servers and user interface. Hospital needs to be highly integrated with the existing hospital information system and then explored the set of processes of physiological signals acquisition, storage, analysis and combination with the ehealth records. Once the system is implemented in the general ward of the hospitals and starts collecting more than 1000cases from the clinics and so continues the whole system repeatedly to give the clinic feedback. With this system helps in reliable physiological monitoring of patients of general ward in the hospitals and generates more personalized pathophysiological signals/information to diagnose the disease and treatment form continuously monitored physiological data.
Throughout the latest 20 years, beat screens (HRMs) have become a for the most part used guide for a grouping of employments [18]. The improvement of new HRMs has in like manner progressed rapidly during the latest two decades. Despite beat (HR) responses, look at has starting late revolved more around beat capriciousness (HRV).
Existing approach for separating heart-related signs fall under two classes: wearable sensor advancements [19] and non-contact (fusing) structures. The conventional framework for checking beat surveyed the spikes of the potential made by heart at each weight beat for example electrocardiogram, or ECG. Physiological estimations, for example, ECG signals, are unbelievable considering the way that they are obliged by means of modified actuations of the autonomic unmistakable structure (ANS).
In any case, existing sensors that can separate these signs require physical contact with subject's body, and causes impedance with the client experience. Various wearable advances, for example, a versatile ECG contraption, worn as a chest band holds the sensor focused over the heart, may move the readings remotely to the host PC. Unfortunately, the chest band is ungainly when worn for extended periods. It has a task to do in helpful and practice settings, at any rate it's unquestionably not a feasible choice for consistent use. Subsequently, wristband or a sharp have inadequacies [20].
Eduardo Jose da S. Luz, et al [
Tian Zhou, et al [
Wei Wei, et al, [
Luis J.Mena et.al [
Deger Ayata, et al [8], The study shows a framework for emotion recognition using multimodal physiological signals from RB, PPG, and FTT. The paper concluded that the decision level fusion
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from multiple classifiers improved the accuracy rate of emotion recognition both for arousal and valence dimensions.
Xiaowei Zhang, et al [9], Final fusion representation exhibits higher class-separability power for emotion recognition.
Table 12
Chen Wang et al [10], Several other approaches have been proposed as signal processing and machine learning. The algorithms analyzed and compared with the public database MAHANOB-HCI.
(independent component analysis)
Note: Mean error (M)-Performance measured; Standard Deviation (SD), RMSE(Root mean squared error) ; ρ-correlation co-efficient
Quentin Meteier et al [13], The author concluded a high level of drivers’ mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.
I have studied last 6years paper and their linked referenced papers too previously cited paper in the literature papers. From all those papers it has been concluded that various types of physiological signals, various parameters as per the papers have been reviewed. The physiological signal (ECG) has been used to detect for some abnormalities in heartbeat classifications, segmentation techniques, and feature descriptions and learning algorithms used. In some papers different datasets used, different modalities, signal extraction and classifiers used, discussion of wearable technologies, and surgical skill task validated with da Vinci Skills Simulator. With different modalities signals found results to individual accuracy and precision four types of physiological signals includes EEG, ECG, Respiration Amplitude RA, and Galvanic Skin Response GSR. Various analysis domains have been used in physiological emotion features extraction.
We are thankful to the department of Electronics and Communication Engineering, National Institute of Technical Teachers Training and Research, Chandigarh for their interest in this work and useful comments to draft the final form of the paper. The support of AICTE QIP (Poly) Schemes department, Government of India is gratefully acknowledged. We would like to thank NITTTR Chandigarh for lab facilities and research environment to carry out this work. 6. References [7] Bassett, D.R. Device-based monitoring in physical activity and public health research. Physiol.
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