Medical Signal Processing

This chapter introduces sleep, the pattern of sleep, wakefulness, disorders associated with sleep, diseases of heart and lungs that can be identified by analysing one's sleep. Sleep is generally equated to the neurological system and the brain. It is believed that sleep can be identified only with EEG. This chapter also explores the usage of EEG in detecting the disorders associated with sleep, and more emphasis is given to the bio signals other than EEG, which includes ECG, PPG, acoustic signals that can be used in understanding the sleep and its related disorders. It explains the biomedical devices that are used for sleep-related studies. This chapter explores the stages of sleep signal processing where the authors have suggested how to reduce noises at the stage of data acquisition. Further topics explore various signal processing methods that need to be adapted in various stages, namely preprocessing, filtering, feature extraction, validation, and automated processing.

Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing

Motivated by analyzing long-term physiological time series, we design a robust and scalable spectral embedding algorithm, coined the algorithm RObust and Scalable Embedding via LANdmark Diffusion (ROSE-LAND). The key is designing a diffusion process on the dataset, where the diffusion is forced to interchange on a small subset called the landmark set. In addition to demonstrating its application to spectral clustering and image segmentation, the algorithm is applied to study the long-term arterial blood pressure waveform dynamics during a liver transplant operation lasting for 12 hours long.

Anomaly Detection from Medical Signals and Images Using Advanced Convolutional Neural Network

In the field of Artificial Intelligence (AI), deep learning is a method falls in the wider family of machine learning algorithms that works on the principle of learning. Convolutional Neural Networks (CNNs) can be used for pattern recognition from different images based on deep learning. Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. Anomaly detection from medical EEG signals based on spectrogram and medical corneal images are tested and evaluated in this paper. Technically, deep learning CNN models are used in the train and test processes, each input image will pass through a series of convolution layers with filters (Kernels), pooling, and fully connected layers (FC) for the classification purposes. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.