Nonlinear features of heart rate variability in paranoid schizophrenic

Cardiovascular mortality is significantly increased in patients suffering from schizophrenia. However, psychotic symptoms are quantified by means of the scale for the assessment of positive and negative symptoms, but many investigations try to introduce new etiology for psychiatric disorders based on combination of biological, psychological and social causes. Classification between healthy and paranoid cases has been achieved by time, frequency, Hilbert–Huang (HH) and a combination between those features as a hybrid features. Those features extracted from the Hilbert–Huang transform for each intrinsic mode function (IMF) of the detrended time series for each healthy case and paranoid case. Short-term ECG recordings of 20 unmedicated patients suffering from acute paranoid schizophrenia and those obtained from healthy matched peers have been utilized in this investigation. Frequency features: very low frequency (VLF), low frequency (LF), high frequency (HF) and HF/LF (ratio) produced promising success rate equal to 97.82 % in training and 97.77 % success rate in validation by means of IMF1 and ninefolds. Time–frequency features [LF, HF and ratio, mean, maximum (max), minimum (min) and standard deviation (SD)] provided 100 % success in both training and validation trials by means of ninefolds for IMF1 and IMF2. By utilizing IMF1 and ninefolds, frequency and Hilbert–Hang features [LF, HF, ratio, mean value of envelope (Formula presented.)] produced 96.87 and 95.5 % for training and validation, respectively. By analyzing the first IMF and using ninefolds, time and Hilbert–Hang features [mean, max, min, SD, median, first quartile (Q1), third quartile (Q3), kurtosis, skewness, Shannon entropy, approximate entropy and energy, (Formula presented.), level of envelope variation (Formula presented.), central frequency (Formula presented.) and number of zero signal crossing (Formula presented.) produced a 100 % success rate in training and 90 % success rate in validation. Time, frequency and HH features [energy, VLF, LF, HF, ratio and (Formula presented.)] provided 97.5 % success rate in training and 95.24 % success rate in validation using IMF1 and sixfolds. However, frequency features have produced promising classification success rate, but hybrid features emerged the highest classification success rate than using features in each domain separately.