Wavelet analysis for early identification of HRV changes in offspring with genetic predisposition to hypertension in Oman.

BACKGROUND: Offspring with a genetic predisposition to hypertension may have higher blood pressure (BP) at rest compared with those without a genetic predisposition to hypertension. They are also expected to have a higher sympathetic component in the heart rate variability (HRV) which could be computed with signal processing algorithms. OBJECTIVE: The purpose of this study is to design a wavelet-based system to estimate the heart rate variability that can be used to detect early cardiovascular changes in offspring with a genetic predisposition to hypertension. Early detection will help in the treatment of those young people. In this work, the relation between the hypertension and the changes in HRV is investigated. METHODS: The frequency domain and time domain analysis of heart rate variability (HRV) are studied to understand their relationship to the autonomic nervous system in offspring with and without a genetic predisposition to hypertension in Oman at resting state. The wavelet-based soft-decision algorithm is used as the spectral analysis tool to obtain different features from the HRV signal and to select the best performing features for detection of hypertension. The main task is to classify between three categories of subjects: 36 subjects with both normotensive parents (ONT), 22 subjects with single hypertensive parent (OHT1), and 11 subjects with both hypertensive parents (OHT2). RESULTS: The summation of the power of bands B4 and B5 of the 32 bands HRV wavelet-based spectrum, which is equivalent to the frequency range (0.046875 Hz-0.078125 Hz), is used as a classification factor among OHT2, OHT1, and ONT groups. The efficiency of classification between ONT and OHT2 is 85.10%, and between OHT1 and OHT2 is 81.81%. The result of classifying between (ONT and OHT1 as one group) and OHT2 is 85.50%. CONCLUSIONS: The work proves that the wavelet-based spectral analysis technique is a successful tool for classifying the three groups of subjects (ONT, OHT1, and OHT2) with different susceptibility for development of hypertension.

Classification of sleep apnea using wavelet-based spectral analysis of heart rate variability.

BACKGROUND: Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of upper airway. Polysomnographic recording is a conventional method for detection of OSA. Although it provides reliable results, it is expensive and cumbersome. Thus, an advanced non-invasive signal processing based technique is needed. OBJECTIVE: The main purpose of this work is to predict the severity of sleep apnea using an efficient wavelet-based spectral analysis method of the heart rate variability (HRV) to classify sleep apnea into three different levels (mild, moderate, and severe) according to its severity and to distinguish them from normal subjects. METHODS: The standard FFT spectrum analysis method and the soft-decision wavelet-based technique are to be used in this work in order to rank patients to full polysomnography. Data of 20 normal subjects and 20 patients with mild apnea and 20 patients with moderate apnea and 20 patients of severe apnea are used in this study. The data is obtained from the sleep laboratory of Sultan Qaboos University hospital in Oman. Four different classification versions have been used in this work. RESULTS: Accuracy result of 90% was obtained between severe and normal subjects and 85% between mild and normal and 75% between severe and moderate and 83.75% between normal and patients. CONCLUSIONS: The VLF/LF power spectral ratio of the wavelet-based soft-decision analysis of the RRI data after a high-pass filter resulted in the best accuracy of classification in all versions.