Machine learning aided classification of tremor in multiple sclerosis.

Background: Tremors are frequent and disabling in people with multiple sclerosis (MS). Characteristic tremor frequencies in MS have a broad distribution range (1-10 Hz), which confounds the diagnostic from other forms of tremors. In this study, we propose a classification method for distinguishing MS tremors from other forms of cerebellar tremors.

Discrimination between parkinsonian tremor and essential tremor using artificial neural network with hybrid features.

Background: Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremors with a certain overlap in the clinical presentation.

Objective: The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors. The features used are of hybrid type obtained from two different algorithms: the statistical signal characterization (SSC) of the signal describing its morphology, and the soft-decision wavelet-decomposition (SDWD) features extracted from the accelerometer and surface EMG signals.

Investigation of heart rate variability of patients undergoing coronary artery bypass grafting using the statistical signal characterization method.

Background: Autonomic function can be estimated non-invasively using heart rate variability (HRV). HRV of patients undergoing coronary artery bypass grafting (CABG) is investigated in time-domain and frequency-domain before and after CABG to study the effect of operation on the status of patients.

Objective: The main purpose of this work is to evaluate the effect of CABG surgery on patients with ischemic heart disease (IHD) before operation, and to monitor the status of patients on day 6 and day 30 after the CABG operation.

A new simple efficient classification technique for severity of sleep apnea with mathematical model and interpretation.

Background: Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of the upper airway. Polysomnographic recording is a conventional method for detection of OSA. Although it provides reliable results, it is expensive and cumbersome.

Cardio-autonomic functions and sleep indices before and after coronary artery bypass surgery.

Background: Earlier studies showed a short-term impairment of cardio-autonomic functions following coronary artery bypass grafting (CABG). There is a lack of consistency in the time of recovery from this impairment. Studies have attributed the post-CABG atrial fibrillation to preexisting obstructive sleep apnea (OSA) without an objective sleep assessment.

A neural network approach for feature extraction and discrimination between Parkinsonian tremor and essential tremor.

Background: Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremor with a certain overlap in the clinical presentation.

Objective: The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors using spectral analysis of tremor time-series recorded by accelerometry and surface EMG signals.

Methods: The Soft-Decision wavelet-based technique is to be used in this work in order to obtain a 16 bands approximate spectral representation of both accelerometer and two EMG signals of two sets of data (training and test).

A new diagnostic test to distinguish tremulous Parkinson's disease from advanced essential tremor.

Background: Clinical distinction between advanced essential tremor and tremulous Parkinson's disease can be difficult.

Methods: In selected power spectra of accelerometric postural tremor recordings on the more affected side of 41 patients with essential tremor and 39 patients with tremulous Parkinson's disease being indistinguishable by tremor frequency, peak power or number of harmonic peaks, waveform asymmetry (autocorrelation decay), and mean peak power of all harmonic peaks were computed. Cutoff for essential tremor-Parkinson's disease distinction was determined by receiver operating characteristics.

Identification of patients with congestive heart failure using different neural networks approaches.

A new technique for identification of patients with congestive heart failure (CHF) from normal controls is investigated in this paper using spectral analysis and neural networks. The identification system consists of two parts: feature extraction part and classification part. The feature extraction part uses the method of approximate spectral density estimation of R-R-Intervals (RRI) data by implementing the soft decision sub-band decomposition technique.

Statistical signal characterization for congestive heart failure patient's classification.

This paper aims at investigating a new technique of time-domain analysis of heart variability (R-R interval (RRI)) for the screening of patients with Congestive Heart Failure (CHF). This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters are: amplitude mean, period mean, amplitude deviation and period deviation.

A soft decision algorithm for obstructive sleep apnea patient classification based on fast estimation of wavelet entropy of RRI data.

A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The classification is done on the whole record as OSA patient or non-patient (normal).

Screening of obstructive sleep apnea based on statistical signal characterization of Hilbert transform of RRI data.

A new technique of time-domain analysis for screening of Obstructive Sleep Apnea (OSA) using R-R interval (RRI) data is investigated. This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters: amplitude mean, period mean, amplitude deviation and period deviation, and their maximum and minimum values are found over a 5-minutes sliding window for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data.