Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach

By Dipali Bansal

Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach focuses on the design and development of a computer-based system to detect and digitally process human ECG, EMG, and carotid pulse waveforms in real time. The indigenous system developed and described in this book allows for an easy-to-interface, simple hardware arrangement for bio-signal detection. The computational functionality of MATLAB is verified for viewing, digital filtration, and feature extraction of acquired bio-signals.

This book demonstrates a method of providing a relatively cost-effective solution to human physiology real-time monitoring, processing, and interpretation that is more realizable and would directly benefit a larger population of patients.

• Presents an application-driven, interdisciplinary, and experimental approach to bio-signal processing with a focus on acquiring, processing, and understanding human ECG, EMG, carotid pulse data and HRV.
• Covers instrumentation and digital signal processing techniques useful for detecting and interpreting human physiology in real time, including experimental layout and methodology in an easy-to-understand manner.
• Discusses development of a computer-based system that is capable of direct interface through the sound port of a PC and does not require proprietary DAQ units and ADC units.
• Covers a MATLAB-based algorithm for online noise reduction, features extraction techniques, and infers diagnostic features in real time.
• Provides proof of concept of a PC-based twin channel acquisition system for the recognition of multiple physiological parameters.
• Establishes the use of Digital Signal Controller to enhance features of acquired human physiology.
• Presents the use of carotid pulse waveforms for HRV analysis in critical situations using a very simple hardware/software arrangement.

• Language: English
• Publisher: Academic Press
• Release date: Jun 15, 2021
• ISBN: 9780128221563

Dipali Bansal

Dr. Dipali Bansal is the Dean of Engineering at Graphic Era (Deemed to be University), Dehradun, India. She is part of a curiosity-driven research group working in the field of bio-signal processing that brings together experimental and theoretical techniques and approaches in acquiring and analyzing human physiological parameters, namely ECG, EMG, EEG signals, using professional tools like MATLAB and LabVIEW. She got her Bachelor’s degree from BIT Sindri and obtained a PhD in Bio-signal Processing from Jamia Millia Islamia Central University, New Delhi, India. In more than two decades of her professional career, she has honed her skills as an academician, a researcher, and also as an administrator.

Book preview

1

Introduction

Abstract

Understanding human physiology is vital to define and design a foolproof analytical method that encapsulates all the minor and significant signals which would lead to the correct diagnosis of a disease. Bio-signal data acquisition and their processing are a precursor for diagnostic system development. An underlying condition or disease not only needs to be diagnosed but also needs to be monitored, and a suitable therapy needs to be provided for recovery and rehabilitation. Real-time acquisition and processing of human physiology have become indispensable as an interdisciplinary tool, which along with advancements in computational algorithms, medical science, signal processing techniques, communication engineering, and big data practices could bridge the gap and promote the universal health goals. This chapter briefly describes typical bio-signal acquisition and processing techniques. Typical case studies presenting recent advancements in the domain and the challenges faced in real-time acquisition and monitoring of bio-signals are also described. This chapter also sets the objective to be achieved which is primarily to gather information on human physiology through an indigenous measurement system that is portable and cost-effective and permits to interpret and detect abnormalities through signal feature extraction algorithm designed using MATLAB®, thus reducing the subjectivity involved in manual and visual diagnosis and enhancing reproducibility.

Keywords

Bio-signals; bio-signal processing; data acquisition; human physiology; real time; MATLAB

Good health is indispensable for sustained social and economic development and to alleviate poverty. Universal health coverage goals of the World Health Organization (WHO) were constituted with the tenet of easy access of quality health care services at reasonable costs. The health care entails prevention, treatment, and rehabilitation and to provide palliative support. These goals can be achieved by an efficient health care system with ample finances so that medicines and equipment are accessible to the masses supported with motivated and trained health care workers.

The WHO, while recognizing the facts, closely monitors more than 1000 health-related indicators statistically through its 194 member states. The member states have been progressively working on Sustainable Development Goals (SDGs). While 17 SDGs were adopted for monitoring in 2015 by the global leadership, looking at the holistic assessment, it was enhanced to 36 health-related SDGs in 2018. The broad vision remains and relates to a World Free of poverty, hunger, disease, and want. SDG3 is the health-related SDG signifying Good Health and Well Being and calls upon member countries to devise an entire ecosystem promoting universal health achievement, pre-empt health emergencies, and effectively promote robust and disease-free populations. The Millennium Development Goal (MDG) has defined certain priority areas, related to maternal and child mortality, providing nutritious food to the needy and the resolution to fight communicable and pandemic prone diseases (World Health Statistics, 2018).

However, it is a no-brainer that a qualified and equitably distributed health force, which is accessible by masses, is the bare minimum requirement to make significant progress toward the SDGs. It is a grave concern that around 75 countries globally have fewer than one physician per 1000 population. Similarly, around 87 countries have less than three nursing and midwives per 1000 population (World Health Organization, Global Health Work Force Statistics, OECD, 2019). The goal to provide basic health support still stands compromised, leave aside responding to any catastrophe and health emergency. This calls for extraordinary and out-of-the-box efforts so that the universal health goals and sustainable development can be attained.

Enhancements in medical diagnostics and better standards of living have resulted in higher life expectancy around the globe. In fact, it has shown an improvement of 5.5 years between 2000 and 2016. However, if we look across the horizon, the average span of life has gone up considerably from a mere 29 years historically to 73 years in 2019 (Global Health Observatory (GBO) Data Released by WHO).

Understanding human physiology is vital to define and design a foolproof analytical method that encapsulates all the minor and significant signals which would lead to the correct diagnosis of a disease. Biomedical signals and their processing are a precursor for diagnostic system development. Further, an underlying condition or disease not only needs to be diagnosed but also needs to be monitored and a suitable therapy needs to be provided for recovery and rehabilitation.

1.1 Rationale

Real-time bio-signal acquisition and processing of human physiology have become indispensable as an interdisciplinary tool, which along with advancements in computational algorithms, medical science, communication engineering, and big data practices could bridge the gap and promote the universal health goals.

Diagnosis, Therapy & Control and Monitoring are three pillars of bio-signal processing in clinical and nonclinical context. In case of diagnosis, pathological conditions or variations in measurements, pointing to the onset of a disease, can be identified by examining a signal information. The signal in many cases can be acquired through noninvasive sensors. The procedures involved are progressively being devised to optimize cost and make them less taxing and painful to the patients. Similarly, the acquisition and analysis of signals are becoming flexible and simpler so that the required degree of automation reaches the far-flung areas across the globe. The diagnosis can be done either at the patient site or the signal can be transmitted off-site, for analysis on a stand-alone PC or a machine within a reasonable time frame. A digital signal processor (DSP) circuit complements an onboard operating system and hardware for filtration of signal and assists large part of decision-making and diagnosis.

Therapy and control normally refers to a treatment that makes a human to feel better after an illness or injury. Digital Signal Processing has a restricted but important therapeutic role in modifying some physiological processes based on the feedback received through an algorithm in real-time or online basis. In some applications, physiological parameter monitoring is done using devices, to recognize and proactively respond to early stress symptoms. Algorithms are tailor-made to assist recovery and performance protocols, for example, in case of monitoring the performance of athletes or providing symptomatic relief to trapped individuals in calamities. For example, pacemakers are used to treat arrhythmias (irregular heart rhythms) or bradycardia where the heartbeats fall to less than 60 beats per minute (Boston Scientific – Pacemakers). These conditions impair the transport of oxygen and essential nutrients to the brain and other parts of the body. Pacemakers with leads in the right atrium and right ventricle are programmed to deliver the required electrical energy to pace the slow heartbeat as and when required. The equipment can also store or transmit information for evaluation and corrective settings later. However, the challenges exist around complex algorithms triggering response, maximum time delay acceptable for action, and low power consumption requirement for these devices, some of which as discussed above are implanted in the human body.

In case of monitoring, using Biomedical signal processing the real-time systems are connected to the patients. The systems including the sensor technologies detect the subtle changes, say in neurological or cardiac signals on display monitors. Some of these parameters could be electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), Pulse, Oxygen saturation level, Heart rate, etc. Regular monitoring is advisable both when adopting preventive and curative strategies; while the former reduces the probability of a disease advancement, the later reduces the chance of death and disability in identified