Biomedical Signal Processing System Research Articles

Biomedical Signal Processing: Understanding Its Importance and Several Fundamental Steps

The study has depicted the analysed interpretation of importance of biomedical signal processing in medical aspects. Secondary data collection method and thematic data analysis process have been used in this study on the basis of peer reviewed journals that make the study more reliable for the readers. Basic concept of biomedical signal processing has been discussed over here with the diagnosis capability and extraction signal information of biomedical signal processing system to detect the physiological disorders. Challenges and opportunities of biomedical signal processing have been focused in this study to evaluate the impact of it in medical ground. Various applications of biomedical signals have been highlighted in this study including ECG, EEG, fEEG and many more devices. Advance technological implication in biomedical signal processing has been considered in this study highlighting the mechanical composition of ECG machine. Some recommendations of modification of biomedical signal processing system has been considered in this study such as increasing of controlling capability of diverse noise signals that can reduce power line interference. Keyword : Biomedical Signal Processing, ECG Machine, Physiological Disorders.

Design of Low-Power 16-Bit R-2R Digital-to-Analog Converter for Effective Biomedical Signal Processing

Digital-to-analog converter (DAC) is a part of various biomedical signal processing systems. DAC is one of the essential blocks optimizing the performance of various analog-to-digital converters (ADCs) such as SAR-ADC, Delta-Sigma ADC. In this paper, a 16-bit DAC is designed and analyzed that can be used in various applications of ADCs specifically useful in effective biomedical data processing. A design of resistor-2 resistor (R-2R) type DAC based on binary weighted resistors is implemented using 16-bit resolution at sampling rate of 500 MS/s. The main components of the design are R-2R ladder, Switch and operational amplifier. During the design of R-2R DAC op-amp, a current mirror circuit is used that provides a single-ended voltage signal without having any losses. In addition to that, it also uses RC coupling for blocking the DC part of the signal and improves the closed loop stability. All the work was carried out by using cadence virtuoso editor tool. The DAC is designed and simulated at 90 and 45 nm CMOS technology and a comparative analysis is done. The result obtained matches the desired specifications of DAC and it is more efficient and can be used for high-speed applications.

A Low Power 12-bit 1-kS/s SAR ADC for Biomedical Signal Processing

In this paper, a 12-bit 1-kS/s successive approximation register analog-to-digital converter (ADC) is presented for biomedical signal processing system. A multi-segmentation digital-to-analog converter architecture and a hybrid switching scheme are proposed to reduce the total unit capacitor count. A new ultra low-leakage switch is proposed for sample-and-hold and capacitor control switch blocks in order to improve conversion accuracy and reduce leakage current. The proposed ADC is fabricated in 130-nm CMOS process with a core area of 0.16 mm2. The measurement results show that the ADC achieves 10.47 ENOB and consumes 110-nW power at a sampling rate of 1 kS/s with 1.0-V supply voltage. A Walden figure of merit of 76 fJ/conversion-step is obtained.

Editorial (Hot Topic: Bioinspired and Biomedical Signal Processing Systems)

Editorial (Hot Topic: Bioinspired and Biomedical Signal Processing Systems)

An Ultra Low Energy Biomedical Signal Processing System Operating at Near-Threshold

This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today.

Systems engineering principles for the design of biomedical signal processing systems

Systems engineering aims to produce reliable systems which function according to specification. In this paper we follow a systems engineering approach to design a biomedical signal processing system. We discuss requirements capturing, specification definition, implementation and testing of a classification system. These steps are executed as formal as possible. The requirements, which motivate the system design, are based on diabetes research. The main requirement for the classification system is to be a reliable component of a machine which controls diabetes. Reliability is very important, because uncontrolled diabetes may lead to hyperglycaemia (raised blood sugar) and over a period of time may cause serious damage to many of the body systems, especially the nerves and blood vessels. In a second step, these requirements are refined into a formal CSP ‖ B model. The formal model expresses the system functionality in a clear and semantically strong way. Subsequently, the proven system model was translated into an implementation. This implementation was tested with use cases and failure cases. Formal modeling and automated model checking gave us deep insight in the system functionality. This insight enabled us to create a reliable and trustworthy implementation. With extensive tests we established trust in the reliability of the implementation.