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Abstract.The air flow during the respiration process produces lung sound and provide information on lung health. Automatic lung sound recognition becomes one of the areas of interest to researchers in the field of biomedical signal processing. Signal complexity measurement becomes one of features extraction method for lung sound analysis. Some signal complexity measurement technique that is often used for example are entropy, fractal dimension, and high-order statistics. In this study conducted multiscale Hjorth descriptor measurements on lung sounds for lung sound classification. The results showed that the complexity on a scale of 1-5 yield 95.06% accuracy. Multiscale analysis succeeded in improving the accuracy of the lung sound classification. The higher the scale that is used does not guarantee to increase the accuracy.

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Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.

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