iagnosis of chronic myocardial infarction (MI) is an important clinical task because the management of and treatment planning for patients is different for chronic MI versus acute MI (1,2). The extent of chronic MI, including location, size, and transmurality, provides rich information for patient diagnosis, prognosis, and therapy planning (3). Therefore, accurate delineation and comprehensive evaluation of chronic MI is of great clinical interest. Late gadolinium enhancement (LGE) MRI has been established as the ground truth reference technique for chronic MI evaluation (4-6). However, including LGE MRI in the MRI examination extends the scanning duration and there are also growing concerns about its safety (7-9). While LGE MRI is contraindicated in patients with severe renal impairment, a recent study has also shown that gadolinium might deposit into the skin, dentate nucleus, and globus pallidus of patients with normal renal function (10). A reliable technique to detect and delineate MI without the need for gadolinium-based contrast agent would therefore be highly desirable. T1 and T2 mapping techniques (11) are non-contrast material-enhanced approaches that show longer T1 and T2 relaxation times in acute MI compared with normal myocardium. In comparison, while T1 relaxation time is

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In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy.