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In Wireless tele-cardiology applications, ECG signal is widely used to monitor cardiac activities of patients. Accordingly, in most e-health applications, ECG signals need to be combined with patient confidential information. Data hiding and watermarking techniques can play a crucial role in ECG wireless tele-monitoring systems by combining the confidential information with the ECG signal since digital ECG data is huge enough to act as host to carry tiny amount of additional secret data. In this paper, a new steganography technique is proposed that helps embed confidential information of patients into specific locations (called special range numbers) of digital ECG host signal that will cause minimal distortion to ECG, and at the same time, any secret information embedded is completely extractable. We show that there are 2.1475 × 10(9) possible special range numbers making it extremely difficult for intruders to identify locations of secret bits. Experiments show that percentage residual difference (PRD) of watermarked ECGs can be as low as 0.0247% and 0.0678% for normal and abnormal ECG segments (taken from MIT-BIH Arrhythmia database) respectively.

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This paper presents a literature survey for electroencephalogram (EEG) signal classification approaches based on machine learning algorithms. EEG classification plays a vital role in many health applications using machine learning algorithms. Mainly, they group and classify patient signals based on learning and developing specific features and metrics. In this paper, 32 highly reputed research publications are presented focusing on the designed and implemented approach, applied dataset, their obtained results and applied evaluation. Furthermore, a critical analysis and statement are provided for the surveyed papers and an overall analysis in order to have all the papers under an evaluation comparison. SVM, ANN, KNN, CNN, LDA, multi-classifier and more other classification approaches are analyzed and investigated. All classification approaches have shown potential accuracy in classifying EEG signals. Evidently, ANN has shown higher persistency and performance than all other models with 97.6% average accuracy.

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