Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions. This research extensively delineates the different heart conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, and their underlying reasons and symptoms and subsequently introduces AI-based detection methodologies for precisely classifying such diseases. The review shows that the incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuracies along with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention, reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimized monitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the review also indicates the conspicuous disparities between the results generated by previous algorithms and the latest ones, paving the way for medical researchers to ascertain the accuracy of these results through comparative analysis with the practical conditions of patients. In conclusion, AI in heart disease detection holds paramount significance and transformative potential to greatly enhance patient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.

Cognitive stress (CS) changes the peripheral attributes of a muscle, but its effect on multi-head muscles has not been investigated. The objective of the current research was to investigate the impact of CS on the three heads of the triceps brachii (TB) muscle. Twenty-five young and healthy university students performed a triceps push-down exercise at 45% one repetition maximum (1RM) with and without CS until task failure, and the rate of fatigue (ROF), endurance time (ET) and number of repetitions (NR) for both exercises were analyzed. In addition, the first and last six repetitions of each exercise were considered non-fatiguing (NF) and fatiguing (Fa), respectively, and the root mean square (RMS), mean power frequency (MPF) and median frequency (MDF) for each exercise repetition were evaluated. The lateral and long head showed significant differences (P<0.05) in the ROF between the two exercises, and all the heads showed significant (P<0.05) differences in the RMS between the two exercises under NF conditions. Only the long head showed a significant difference (P<0.05) in the MPF and MDF between the two exercises. CS increases the ET (24.74%) and NR (27%) of the exercise. The three heads showed significant differences (P<0.05) in the RMS, MPF and MDF under all exercise conditions. A lower ROF was obtained with CS. In addition, the RMS was found to be better approximator of CS, whereas MPF and MDF were more resistant to the effect of CS. The results showed that the three heads worked independently under all conditions, and the non-synergist and synergist head pairs showed similar behavior under Fa conditions. The findings from this study provide additional insights regarding the functioning of each TB head.

The objective of this study was to investigate the effects of changes in exercise intensity and speed on the three heads of the triceps brachii (TB) during triceps push-down exercise until task failure. Twenty-five subjects performed triceps push-down exercise at three different intensities (30, 45, and 60% 1RM) and speeds (slow, medium, and fast) until failure, and surface electromyography (sEMG) signals were recorded from the lateral, long and medial heads of the TB. The endurance time (ET), number of repetitions (NR) and rate of fatigue (ROF) were analyzed. Subsequently, the root-mean-square (RMS), mean power frequency (MPF) and median frequency (MDF) under no-fatigue (NF) and fatigue (Fa) conditions were statistically compared. The findings reveal that ROF increases with increase in the intensity and speed, and the opposite were obtained for the ET. The ROF in the three heads were comparable for all intensities and speeds. The ROF showed a significant difference (P < 0.05) among the three intensities and speeds for all heads. The three heads showed significantly different (P < 0.05) MPF and MDF values for all the performed exercises under both conditions, whereas the RMS values were significantly different only under Fa conditions. The current observations suggest that exercise intensity and speed affect the ROF while changes in intensity do not affect the MPF and MDF under Fa conditions. The behavior of the spectral parameters indicate that the three heads do not work in unison under any of the conditions. Changes in the speed of triceps push-down exercise affects the lateral and long heads, but changes in the exercise intensity affected the attributes of all heads to a greater extent.

Objective: The objective of this study was to investigate fatigue in the three heads of the triceps brachii (TB) muscle using surface electromyography (sEMG) obtained at 30%, 45% and 60% of maximal voluntary contraction (MVC). Methods: Twenty-five subjects performed isometric elbow extension until failure, and the rate of fatigue (ROF), time to fatigue (TTF) and normalized TTF (NTTF) were statistically analysed. Subsequently, the behaviour of root-mean-square (RMS), mean-power frequency (MPF) and median-power frequency (MDF) under pre-, onset- and post-fatigue conditions were compared. Results: The findings indicated that, among the heads, ROF was statistically significant at 30% and 45% MVC (P0.05). For every head, only TTF was statistically significant (P<0.05) at different intensities. MPF and MDF under pre-, onset- and post-fatigue conditions were statistically significant (P<0.05) among the heads at all intensities, whereas RMS showed no such behaviour. Conclusion: The investigated parameters reveal that the three heads of TB act independently before fatigue onset and appear to work in union after fatigue. Synergist head pairs exhibit similar spectral and temporal behaviour in contrast to the non-synergist TB head pair. We find spectral parameters to be more specific predictors of fatigue.

This research work will examine singular value decomposition (SVD), Fast Fourier Transform (FFT) and least square technique for the estimation of harmonics in power system. Harmonics in power system disturbs the quality of energy supplied to load, increased energy losses and reduce the reliability of the power system. In some cases, the large power converter systems produce not only typical harmonics but also a significant amount of inter harmonics which can significantly deteriorate the quality of power supply voltage. Inter harmonics are those components of current or voltage waveform which are not integer multiple of fundamental frequency component. Since poor power quality cause losses in power system that's why researchers are working to find techniques to estimate and control the harmonics in power system. Singular value decomposition approach is an ideal technique to estimate harmonics in power system located far away. Since in most of the cases, power system produce over determine equations which can easily be solved using SVD for optimal solution. Singular vector decomposition, SVD is really a mathematical method to lessen a dataset containing an extensive number of qualities to a dataset containing fundamentally less values, yet which still contains a huge portion of the variability introduce in the first information. SVD investigation brings about a more reduced representation of correlations, particularly with multivariate datasets and can give knowledge into spatial and temporal variations exhibited in the fields of data being analysed under different circumstance. Similarly traditional least square method is also one of the most prominent method to estimate the line of best fit. This research work will show the analysis of said techniques to find the optimal approximation of noisy and distorted wave form. To investigate effectiveness of said techniques; simulations will be carried out in MATLAB with same parameters.