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Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.

Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.

From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.

The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

In order to be able to use the aroma hand massage as a skill that can be done by a nurse who does not have a special aromatherapy technique, we examine anti-stress effects of simplified aroma hand massage for healthy subjects. We evaluated the anti-stress action of aroma hand massage and the different components of the procedure in 20 healthy women in their twenties. We used autonomic nervous function measured via electrocardiogram as an index of stress. After conducting a baseline electrocardiogram, we induced stress in the participants by asking them to spend 30 minutes completing Kraepelin's arithmetic test. We then administered various treatments and examined the anti-stress effects. Kraepelin's test significantly increased sympathetic nervous function and significantly reduced parasympathetic nervous function. Compared with massage without essential oil or aroma inhalation, aroma hand massage significantly increased parasympathetic nervous function and significantly decreased sympathetic nervous function. The effect of the aroma hand massage persisted when the procedure was simplified. The anti-stress action of the aroma hand massage indicates that it might have beneficial application as a nursing technique. There are several limitations in this study; ambiguities of low component/high component ratio of heart rate variability and bias by small subjects groups of the same women.

The purpose of this paper is to provide appropriate methods for reducing the abnormalities on the extracted fetal heart signal from the maternal electrocardiogram (ECG).

In this regard, the extracted signal of the fetal heart from the mother, improved using an active noise cancelation (ANC) system. It uses commonly adaptive algorithms of normalized least mean squares (NLMS). In the present paper, fetal extraction and denoising methodology are proposed. This methodology uses a combination of the NLMS algorithm with Savitzky–Golay (S-G) filter.

The obtained results show that a combination of NLMS algorithm with filter coefficient of 15 and µ = 0.02 and S-G filter has a better qSNR (qSNR = 3.6727) and good performance for fetal ECG extraction in comparison with the other works for average fmSNR in the range of −30 to −15 dB. Also, with considering the SNR value of −24.7 dB before filtering and SNR = 3.1861 dB after filtering; the SNR improvement of 27.8861 dB has been obtained.

A new method in the extract and noise reduction of fetal ECG from maternal ECG by the combination of NLMS algorithm and S-G filter is proposed.

The purpose of the current study is to explore the potential possibility of acceleration in recognition, remedial process of heart disease and continuous electrocardiogram (ECG) signal acquisition. The textile-based ECG electrode is prepared by inkjet printing of activator followed by electroless plating of nickel (Ni) particle.

The electrical resistance shows a range of around 0.1 Ω/sq, which sounds quite proper for ECG signal acquisition, as the potential difference according to heart activity on skin surface is in milivolt range. Surface modifications of Ni–phosphorus (P)-plated polyester fiber were studied by scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffractionmethods. The quality of the acquired signal from printed square-shaped sensors in two sizes with areas of 9 and 16 cm2 compared with the standard Ag/Agcl electrode using commercial ECG with the patient in the sitting position.

Comparison of these data led to the consideration of small fabric sensor for better performance and the least disturbance regarding homogeneity and attenuation in electric field scattering. Using these types of sensors in textile surface because of flexibility will provide more freedom of action to the user. Wearable ECG can be applied to solve the problems of the aging population, increasing demand for health services and lack of medical expert.

In the present research, a convenient, inexpensive and reproducible method for the patterning of Ni features on commercial polyester fabric was investigated. Printed designs with high electrical conductivity can be used as a cardiac receiving signals’ sensor.

The purpose of this paper is to analyze the results of recording electrophysiological signals by nanosensors during tests on volunteers using neutral questions and questions that cause excitement.

The nanosensor-based hardware and software complex (HSC) was used for simultaneous recording of electrocardiogram, electroencephalogram and galvanic skin response during tests on volunteers using neutral questions and questions that cause excitement. The recording was carried out in real time without averaging and filtering in the extended frequency range from 0 to 10,000 Hz, level of more than 1 µV and sampling frequency equal to 64 kHz.

For the first time, the following signals were recorded by nanosensors without filtering and averaging in the measuring channels: real-time micropotentials on an electrocardiogram with a duration of 0.2 ms and a level of 1 µV or more. Also, for the first time, changes in the shape and amplitude of the P wave, slow waves on the electroencephalography (EEG), high impulse activity of the EEG and impulse activity of short duration on the GSR were recorded in response to questions that cause excitement.

The obtained results will be used for high-resolution equipment to develop additional measuring channels in existing types of equipment for psychophysiological studies.

For the first time, new data undistorted by filters was obtained on the amplitude and time parameters of electrophysiological signals in the frequency range from 0 to 10,000 Hz in response to questions that cause excitement, which was due to high sensitivity and noise immunity of nanosensors in comparison with existing electrodes for biopotential recording.

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

Biometric scans using fingerprints are widely used for security purposes. Eventually, for authentication purposes, fingerprint scans are not very reliable because they can be faked by obtaining a sample of the fingerprint of the person. There are a few spoof detection techniques available to reduce the incidence of spoofing of the biometric system. Among them, the most commonly used is the binary classification technique that detects real or fake fingerprints based on the fingerprint samples provided during training. However, this technique fails when it is provided with samples formed using other spoofing techniques that are different from the spoofing techniques covered in the training samples. This paper aims to improve the liveness detection accuracy by fusing electrocardiogram (ECG) and fingerprint.

In this paper, to avoid this limitation, an efficient liveness detection algorithm is developed using the fusion of ECG signals captured from the fingertips and fingerprint data in Internet of Things (IoT) environment. The ECG signal will ensure the detection of real fingerprint samples from fake ones.

Single model fingerprint methods have some disadvantages, such as noisy data and position of the fingerprint. To overcome this, fusion of both ECG and fingerprint is done so that the combined data improves the detection accuracy.

System security is improved in this approach, and the fingerprint recognition rate is also improved. IoT-based approach is used in this work to reduce the computation burden of data processing systems.

According to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.

In this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.

Among these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.

In this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.

The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.

The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.

Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.

The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.