Effect of aerobic conditioning on ventricular activation: a principal components analysis approach to high-resolution electrocardiogram

PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications

In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the proposed architecture is validated with a novel hardware model for detecting Chronic Kidney Disease (CKD) from saliva samples. For detecting CKD, the urea concentration in the saliva sample is monitored by converting it into ammonia. The urea on hydrolysis in the presence of urease enzyme produces ammonia. This ammonia is then measured using a semiconductor gas sensor. The sensor response is given to the proposed architecture for feature extraction and classification. The performance of the architecture is optimized by regulating the parameter values using a PSO algorithm. The proposed architecture outperforms current conventional methods, as this approach is a combination of strong feature extraction and classification techniques. Optimal features are extracted directly from the raw signal, aiming to reduce the computational time and complexity. The proposed architecture has achieved an accuracy of 98.25%.

Assessment of Autonomic Function by Phase Rectification of RRInterval Histogram Analysis in Chagas Disease

Background

In chronic Chagas disease (ChD), impairment of cardiac autonomic function bears prognostic implications. Phase‑rectification of RR-interval series isolates the sympathetic, acceleration phase (AC) and parasympathetic, deceleration phase (DC) influences on cardiac autonomic modulation.

Objective

This study investigated heart rate variability (HRV) as a function of RR-interval to assess autonomic function in healthy and ChD subjects.

Methods

Control (n = 20) and ChD (n = 20) groups were studied. All underwent 60-min head-up tilt table test under ECG recording. Histogram of RR-interval series was calculated, with 100 ms class, ranging from 600–1100 ms. In each class, mean RR-intervals (MNN) and root-mean-squared difference (RMSNN) of consecutive normal RR-intervals that suited a particular class were calculated. Average of all RMSNN values in each class was analyzed as function of MNN, in the whole series (RMSNN_T), and in AC (RMSNN_AC) and DC (RMSNN_DC) phases. Slopes of linear regression lines were compared between groups using Student t-test. Correlation coefficients were tested before comparisons. RMSNN was log-transformed. (α < 0.05).

Results

Correlation coefficient was significant in all regressions (p < 0.05). In the control group, RMSNN_T, RMSNN_AC, and RMSNN_DC significantly increased linearly with MNN (p < 0.05). In ChD, only RMSNN_AC showed significant increase as a function of MNN, whereas RMSNN_T and RMSNN_DC did not.

Conclusion

HRV increases in proportion with the RR-interval in healthy subjects. This behavior is lost in ChD, particularly in the DC phase, indicating cardiac vagal incompetence.

Cardiac autonomic changes in middle-aged women: identification based on principal component analysis

The purpose of this study was to investigate the application of the principal component analysis (PCA) technique on power spectral density function (PSD) of consecutive normal RR intervals (iRR) aiming at assessing its ability to discriminate healthy women according to age groups: young group (20-25 year-old) and middle-aged group (40-60 year-old). Thirty healthy and non-smoking female volunteers were investigated (13 young [mean ± SD (median): 22·8 ± 0·9 years (23·0)] and 17 Middle-aged [51·7 ± 5·3 years (50·0)]). The iRR sequence was collected during ten minutes, breathing spontaneously, in supine position and in the morning, using a heart rate monitor. After selecting an iRR segment (5 min) with the smallest variance, an auto regressive model was used to estimate the PSD. Five principal component coefficients, extracted from PSD signals, were retained for analysis according to the Mahalanobis distance classifier. A threshold established by logistic regression allowed the separation of the groups with 100% specificity, 83·2% sensitivity and 93·3% total accuracy. The PCA appropriately classified two groups of women in relation to age (young and Middle-aged) based on PSD analysis of consecutive normal RR intervals.