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- The biological signal processing and analysis techniques contain the design of signal preprocessing tools for portable wearable sensors, artifact cancellation methods for signal quality improvement, nonlinear analysis for the representation of signal complexity or dynamics, feature extraction using time-frequency analysis or statistical models, pattern classifications, and computer-aided diagnosis based on deep learning neural networks or other computational algorithms, along with well-devised...
www.ncbi.nlm.nih.gov/pmc/articles/PMC9319153/Biological Signal Processing and Analysis for Healthcare ...
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Feb 13, 2013 · Biophysical signal processing algorithms have been developed by experimentalists to solve problems specific to their own questions of interest.
- Max A. Little, Nick S. Jones
- 2013
discuss generic characteristics of biophysical signals, and the common mathematical principles behind the design of the disparate algorithms used to investigate their step-like character have received little attention. On the empirical side, these algorithms have rarely been tested head-to-head.
- Max A. Little, Nick S. Jones
- 2013
Jul 30, 2021 · This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals.
- Radek Martinek, Martina Ladrova, Michaela Sidikova, Rene Jaros, Khosrow Behbehani, Radana Kahankova,...
- 10.3390/s21155186
- 2021
- Sensors (Basel). 2021 Aug; 21(15): 5186.
- 1 interference and Noise
- 2 Biomedical Signal Processing
- 3 Biomedical Signal Recognition
In the process of recording these biological signals, some unwanted signals, called noises, are also picked up. These noises may be inherent to the measurement equipment or may be generated by other systems in the vicinity of the recording. In physiological measurement, these unwanted noises will have a significant impact on their analysis and inte...
The core of biomedical signal processing is to obtain life system status information extracted from biological and physiological systems. Therefore, the monitoring and interpretation of this information have an important diagnostic value for clinicians and researchers to obtain information related to human health and diseases. Biomedical signals ar...
1.5.3.1 Electroencephalogram
The brain is the most important organ of the human body. The electrical signals generated represent the function of the brain and the state of the entire body. Therefore, the electroencephalogram (EEG) signal obtained from the brain should also correspond to the state of the subject’s entire body. The neurophysiological characteristics of the brain and the correlation of the generated signals need to be understood to obtain the basic working principles, and signal processing techniques should...
1.5.3.2 Electrocardiograph
The human heart is the most important muscle of the human body. It forms the cardiovascular system with blood vessels and pumps blood into every cell of the body. An electrocardiograph (ECG) is a noninvasive instrument that captures the electrical activity of the heart and displays irregular heartbeats. Therefore, the electrocardiograph is an important tool for judging the health and function of the cardiovascular system. The electrocardiograph (ECG) signal is derived from the electrical acti...
1.5.3.3 Electromyography
The responsibility of the human musculoskeletal system is to obtain the power needed to perform various activities. The system is composed of the nervous system and the muscular system, which together constitute the neuromuscular system. The movement and alignment of the limbs are controlled by electrical signals transmitted back and forth between the muscles and the peripheral and central nervous systems. Neural information is encoded as an action potential rate (discharge rate) that is prop...
Jul 18, 2022 · In this Special Issue, we strive to highlight the state-of-the-art signal processing technologies that are suited for multi-modal physiological data integration and fusion to generate comprehensive and clinically actionable information.
Feb 22, 2024 · The text consists of some 14 chapters subdivided into four sections: Physiological signal processing; EEG-ECG signal processing; Gait, balanace signal processing; and Wearables, sensors signaling. This text is replete with acronyms, some of which are not defined.
May 1, 2024 · BioSPPy is one of the first ever Python libraries created for physiological signal processing. As shown in Table 1, it extends the libraries available in the state-of-the-art across varied dimensions. For data loading, BioSPPy stands out by enabling data loading across a widespread set of input types (TXT, JSON, to HDF5).
