We found a significant increase in salivary antioxidant potential and oxytocin after pleasant tactile stimulation for the participants compared to the control group. Salivary oxytocin and antioxidant capacity were quantified before and after the touch test. Heart rate variability and low-frequency/high-frequency ratios from the power spectral density of ECG were determined to assess psychoemotional relaxation. They ranked the touch pleasantness on a scale from 1 to 10. The participants were subjected to tactile stimulation using a specially designed protocol. The control group consisted of 24 volunteers. A total of 56 healthy volunteers aged 18–38 years participated in the study. Our research is aimed to reveal the correlations between the subjectively perceived pleasantness of touch, the antioxidant potential of saliva, and salivary oxytocin. This indicates that the proposed method may be used by doctors and medical personnel, to assist their diagnosis of patients by integrating the proposed method with currently available medical devices such as an electronic stethoscope.Ī pleasant touch reduces psychoemotional stress via the oxytocin mechanism due to its anti-inflammatory and antioxidant effects. Results indicated that the proposed method was able to classify normal and abnormal heart sounds from PCG signals with high accuracy. LDA/ANN with time domain and frequency domain features achieved 90% and 83.33%, respectively, whilst the combined time and frequency domain features achieves 93.33%. On the other hand, the proposed LDA/ANN classification method achieves better results than the LDA/NCC method. The normal and abnormal heart sound classification accuracies of the LDA/NCC method are 50%, 33.33%, and 53.33% for time domain, frequency domain, and the combination of both time and frequency domain features, respectively. The LDA low dimensional features are classified using the Nearest Centroid Classifier (NCC) and Artificial Neural Network (ANN) classifiers. Supervised Linear Discriminant Analysis (LDA) dimensionality reduction algorithm is applied to the time and frequency domain features, to reduce the number of features and to identify the most reliable features that can help in the classification of the heart sounds.

The selected time and frequency domain features are further expanded using their statistical values of maximum, minimum, average, median, and standard deviation. The time domain features include prominent peak amplitude, systole and diastole timings, cycle times, area under prominent peaks, and intensity, whilst the frequency domain features are the 20 filter bank energies of Mel Frequency Cepstral Coefficients (MFCC). The PCG signals are first segmented to identify S1 and S2 prominent peaks, before features from the segmented PCG signals are extracted in both time and frequency domains. In this research, an automatic heart sound (normal and abnormal) identification method using the first (S1) and second (S2) heart sound features present in Phonocardiography (PCG) signals, has been proposed. Different features time domain, frequency domain, and non-linear feature analysis, provide a major impact on heart sound classification. The integration of signal processing with feature extraction and different other machine learning techniques, are new research trend in the studies of heart sound analysis. An intelligent support system is needed to help the identification of abnormalities of their hearts.

Heartbeat sound