Effective electromyographic (EMG) signal characterization is critical in the diagnosis of
neuromuscular disorders. Machine-learning based pattern classification algorithms are …

Decision tree algorithms are extensively used in machine learning field to classify
biomedical signals. De-noising and feature extraction methods are also utilized to get higher …

The machine learning based techniques for detection of lungs cancer can assist the
clinicians in assessing the risk of pulmonary nodules being malignant. We are developing …

The adaptability of heart to external and internal stimuli is reflected by the heart rate
variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes …

Purpose This study used machine learning classification of texture features from MRI of
breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular …

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the
brain. The research reveals that brain activity is monitored through electroencephalogram …

Electromyographic (EMG) signal analysis plays a vital role in diagnosing neuromuscular
disorders (NMD). It is based on the clinician's experience in interpreting the signal's shape …

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer
can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and …

Objective Distinguishing normal, neuropathic and myopathic electromyography (EMG)
traces can be challenging. We aimed to create an automated time series classification …

Different approaches have been applied for quantitative analysis of EMG signals. This study
introduces the effect of Multiscale Principal Component Analysis (MSPCA) denoising …