China
Africa
Explore chapters and articles related to this topic
Data Compression in Health Monitoring
Published in Rajarshi Gupta, Dwaipayan Biswas, Health Monitoring Systems, 2019
Sourav Kumar Mukhopadhyay, Rajarshi Gupta
Maintaining the quality of the reconstructed signal at a predetermined level is a very important criterion of any biosignal compression algorithm. A strict lossless compression algorithm does not lose any clinical information. Hence, there is no point in worrying about the quality [10]. On the contrary, the theme of any transformation-based biosignal compression algorithm is to convert the original biosignal into some other domain, then discard the comparatively less significant coefficients using a threshold-based technique, and finally, encode the rest of the coefficients in a lossless or near-lossless fashion such as Huffman coding, RLE. Therefore, the distortion or loss, which enters into the signal upon reconstruction, is mostly due to the transformation and thresholding operations. In the cases where the remaining coefficients after thresholding out the less significant ones are compressed using lossless compression methods, it is possible to estimate the amount of distortion which would be present in the reconstructed signal accurately, and near-perfectly if the coefficients are compressed using near-lossless techniques. PRD, PRDN, or any other metrics, which are discussed in Section 3.3.1, could be used as the measures of distortion. WEDD could also be used but only for ECG.
Smart Firefighting Clothing
Published in Guowen Song, Faming Wang, Firefighters’ Clothing and Equipment, 2018
Other biosignals that are frequently measured for medical purposes are galvanic skin response (GSR), electroencephalogram (EEG), electromyogram (EMG), sweat pH, and blood oxygen saturation (Cho et al., 2010; Jeong & Yoo, 2010). Galvanic skin response represents electrical conductivity between two points on the user’s arm, and it is affected by the sweat from physical activity and by emotional stimuli (Solaz et al., 2006). Electroencephalography is a method for measuring electrical activities of the brain by using electrodes along the scalp skin, which is used in medical diagnosis as well as neurobiological research. Kumar and Thilagavathi (2014) developed textile EEG electrodes using a layered structure with conductive and nonconductive fabrics. Performed laboratory tests indicated that those electrodes can be used for high-quality recordings even with human beings with at least thin hair. Such electrodes may be useful in brain–computer interfaces or detection of drowsiness, and their textile structure will be more comfortable for the user. Another kind of sensor for biosignal monitoring, electromyogram sensors, enables monitoring of muscle activity to prevent musculoskeletal disorders. An exemplary solution of such contactless sensor developed within Context project is presented in Figure 11.6 (Taelman et al., 2006).
Topological Data Analysis of Biomedical Big Data
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
Angkoon Phinyomark, Esther Ibáñez-Marcelo, Giovanni Petri
To capture and describe the variability and complexity of biosignals and images acquired from human systems for biomedical applications, a massive amount of information is necessary. The collection of big volumes of biosignal and image data is therefore the first, crucial step in modern science. Thanks to recent developments in low-cost commercial products of wireless and wearable biosignal devices (e.g., EMOTIV1 for recording brain activity signal and Myo2 for recording muscle activity signal) as well as public big biosignal and image resources (e.g., TUH-EEG database [1], which comprises approximately 22,000 electroencephalography (EEG) records from 15,000 patients, or the HCP database [2] which consists of 76 terabytes of behavioral and magnetic resonance imaging data from 1200 healthy subjects), we are being ushered into the era of Big Data. To translate this huge amount of information into a better understanding of the basic biomedical mechanisms and to further biomedical applications, analytic tools, and techniques to analyze Big Data are needed. The name “Big Data” itself only contains a term related to the volume of data while there are other important features of Big Data such as variability of data sources, veracity of the data quality, and velocity of processing the data [3]. These hallmarks of Big Data need to be characterized by special analytic tools and techniques as well. This additional signal processing and classification stage is very important to turn any collected large data set into meaningful biomedical applications.
Wearable electronic textiles
Published in Textile Progress, 2019
David Tyler, Jane Wood, Tasneem Sabir, Chloe McDonnell, Abu Sadat Muhammad Sayem, Nick Whittaker
Biosignals can be further classified based on their occurrence, i.e. permanent or induced biosignals [105]. Permanent signals that exist at all time within the body, are generated without any artificial trigger, impact or excitation from outside of the body; examples are ECG and PCG signals. Induced biosignals are artificially triggered, and they exist roughly for the duration of the excitation, as in the Electroretinogram (ERG). Biomedical sensors that sense biosignals or biopotentials can be categorised as physical, electrical or chemical depending on their specific applications [104]. Different kinds of specialised electrodes are used for capturing such biosignals and may be either non-invasive (applied to the surface of the skin) or invasive (for example, penetrating microelectrodes or wire electrodes). Adding electrodes and sensors to textiles and garments is a non-invasive way of capturing and measuring biosignals. Among the different sensors integrated into SeCS, some of them, such as ECG, EMG and temperature sensors, can be directly developed on textile surfaces [106].
Novel Biometric Approach Based on Diaphragmatic Respiratory Movements Using Single-Lead EMG Signals
Published in IETE Journal of Research, 2023
Beyza Eraslan, Kutlucan Gorur, Feyzullah Temurtas
Bioelectric signals are electrical signals that can measure changes in electrical potential between cells. These signals have low frequency and low amplitude [12, 13]. Examples of biosignals include electromyogram (EMG), electrocardiogram (ECG) and electroencephalogram (EEG), electrooculography (EOG) signals [14, 15]. EMG signals are bioelectric signals that can be detected from the skin surface, formed by the contraction of muscles [1, 14–16].
A low-power, low-offset, and power-scalable comparator suitable for low-frequency applications
Published in International Journal of Electronics, 2023
Riyanka Banerjee, M. Santosh, Jai Gopal Pandey
The modern world has undergone a revolution due to the invention of battery-powered Internet of Things (IoT) gadgets. Applications such as home automation, biomedical instrumentation, precision agriculture, autonomous vehicles, safety, security increase the requirement of lower power consumption capabilities and moderate operation with battery discharge voltage, as explained in Yaqoob et al. (2019), Kodali and Yerroju (2018), Thakur et al. (2021), Guo et al. (2021), Abu et al. (2022), and Du et al. (2022). Such applications operate in a lower bandwidth regime, i.e., from near DC to 10 kHz Webster (2009). Biosignals, such as an electrocardiogram (ECG), an electroencephalogram (EEG), and an electrooculogram (EOG), are low-amplitude and low-frequency electrical signals that operate up to 20 kHz, as given by Wang and Hung (2020) Martinek et al. (2021). Therefore, as mentioned in Zhang et al. (2010) and Khong et al. (2019), biosignals do not require high-speed ADCs. With rapid advancement in biomedical instrumentation, lightweight and compact devices are being developed to operate in low-voltage regions, and thus, as mentioned by LEE et al. (2005) the number of battery cells can be decreased. Invasive devices such as implantable pacemakers with ultra low-power ADCs are now in high demand, where extended battery life is required to operate the device for many years, as explained in Nuzzo et al. (2006). Similarly, according to Teo et al. (2007), smart wireless healthcare monitoring systems require ultra-low power consumption. In de La Fuente-Cortes et al. (2017) mentioned that these wireless devices monitor the bodies of patients and continuously track biosignals before transmitting them. These devices digitise data through read-out interface circuits with ADCs as vital components. In ADC architectures, the comparator is the critical component. de La Fuente-Cortes et al. (2017) used Successive Approximation Algorithm to decide on a voltage comparator that shows the impact of process and temperature (PT) variations on the voltage comparator in a conventional SAR – ADC architecture. Therefore, to improve the overall performance of the SAR-ADC and the circuit response, a symmetric topology is used.