In this chapter, the effect of an old generation romantic music (stimulus) on the autonomic nervous system (ANS) activity and the cardiac electrophysiology of Indian male volunteers was investigated. Electrocardiogram (ECG) signals were acquired and the corresponding RR intervals (RRIs) were extracted. The recurrence analysis of the RRI time series suggested a more stable heart rate in the post-stimulus condition. Heart rate variability (HRV) analysis detected a dominant parasympathetic activity in the post-stimulus condition. The time-domain and the wavelet transform analyses of the ECG signals predicted an alteration in the electrical activity of the heart because of the exposure to the music stimulus. The classification of the HRV and the ECG parameters was performed using artificial neural network (ANN), which resulted in an accuracy of ≥80%.
An ECG is a diagnostic technique that examines and records the heart’s electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study’s primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal’s amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.
The capacity for which the value or is its lower bound is referred to as the optimal channel capacity. Through this communication, we attempt to study the additive Gaussian interference that arises from the Gaussian channel's two-sided state information, which is non-causally known at both the transmitter and receiver and reliant on the channel's noise and input . Verma entropy maximized using normal distribution is noted by the author. He contrasts Verma's channel capacity with Shannon's channel capacity during this investigation, which has implications for communication technology.
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