Effect of a Romantic Song on the Autonomic Nervous System and the Heart of Indian Male Volunteers

Literature Review

The current section discusses some of the recent developments in the field of recurrence-based RRI time series analysis, HRV analysis, and wavelet-based joint time-frequency analysis of ECG signals. Schlenker et al. (2016) have proposed recurrence analysis as a tool for the diagnosis of the ANS dysfunction that is responsible for vasovagal syncope. ECG signals were recorded during orthostatic test from the patients, experiencing vasovagal syncope. Sequences of RRIs were analyzed using frequency-domain, time domain and recurrence analysis methods. Both time domain analysis and recurrence analysis showed significant changes in patients experiencing vasovagal syncope as compared to healthy patients. As the time domain HRV becomes problematic for short term ECG signal so the authors proposed that recurrence analysis can be considered as an important tool for detecting the ANS dysfunction in person having vasovagal syncope (Schlenker & Nedělka, 2013). Houshyarifar et al. (2017) have proposed a method to predict the sudden cardiac death, which is based on the features extracted from recurrence plot, poincarè plot of HRV signal. The method consisted of four stages, i.e., preprocessing, feature extraction, feature reduction and classification. Seven features were extracted from recurrence plot and poincarè plot. These features are then reduced to one feature by linear discrimination technique. Support vector machine (SVM) was used to classify the HRV signals. The proposed method could predict the occurrence of sudden cardiac death 5 min before the sudden cardiac death with an accuracy of 92% (Houshyarifar & Amirani, 2017).