Biomedical signal acquisition, processing and transmission using smartphone

Blood pressure (BP) and the electrocardiographic (ECG) signal, or electrical signal of the heart, are physical measurements that provide insight into the behavior of the cardiac system. This paper presents a novel methodology, where for the first time the relationship between BP and ECG signal is shown. Initially, to perform this study, a signal sampling of ECG signals was performed on 20 patients: eighteen healthy, between 17 and 26 years old, and two with normal BP between 50 and 78 years old. Powerlab equipment was used to record the ECG signal, with electrodes used to capture the heart signal through the lead. Once the signal samples were obtained, the R and T waves in particular were studied with the aim of reading the systolic and diastolic blood pressure separately. In order to obtain the BP with the ECG signals, we used a wavelet transform to identify the R waves and T waves, and then to perform segmentation on the signal and extract the systole and diastole portions from the original signal. Following this procedure, neural networks were applied in order to have a system with systolic and diastolic pressure values based on the ECG signals. This application led to a total success rate of 97.305% for systole and 95.65% for diastole. In conclusion, this article can be said to demonstrate the existence of a relationship between BP and ECG signals. Blood pressure (BP) and the electrocardiographic (ECG) signal, or electrical signal of the heart, are physical measurements that provide insight into the behavior of the cardiac system. This paper presents a novel methodology, where for the first time the relationship between BP and ECG signal is shown. Initially, to perform this study, a signal sampling of ECG signals was performed on 20 patients: eighteen healthy, between 17 and 26 years old, and two with normal BP between 50 and 78 years old. Powerlab equipment was used to record the ECG signal, with electrodes used to capture the heart signal through the lead. Once the signal samples were obtained, the R and T waves in particular were studied with the aim of reading the systolic and diastolic blood pressure separately. In order to obtain the BP with the ECG signals, we used a wavelet transform to identify the R waves and T waves, and then to perform segmentation on the signal and extract the systole and diastole portions from the original signal. Following this procedure, neural networks were applied in order to have a system with systolic and diastolic pressure values based on the ECG signals. This application led to a total success rate of 97.305% for systole and 95.65% for diastole. In conclusion, this article can be said to demonstrate the existence of a relationship between BP and ECG signals.

Biomedical signal and image processing is the study of the dynamic behavior of various bio-signals, which benefits academics and research. Signal processing is used to assess the behavior of analogue and digital signals for the assessment, reconfiguration, improved efficiency, extraction of features, and reorganization of patterns. This paper unveils hidden characteristic information about input signals using feature extraction methods. The main feature extraction methods used in signal processing are based on studying time, frequency, and frequency domain. Feature exaction methods are used for data reduction, comparison, and reducing dimensions, producing the original signal with sufficient accuracy with a structure of an efficient and robust pattern for the classifier system. Therefore, an attempt has been made to study the various feature extraction methods, feature transformation methods, classifiers, and datasets for biomedical signals.

Continuous and steady running of health status remote monitoring systems is essential not to omit episodes of acute exacerbation of chronic disease. Running time of such systems is largely determined by performance capabilities of the patient's wearable system elements. To ensure its long-term operation and efficient performance, the monitoring system must have multilayered structure with the elements realizing recording and picking off biomedical signals, signal processing and analysis, estimation of patient current condition, dynamics of the disease and its prognosis. For this purpose, it is necessary to use smart monitoring algorithms. A specific feature of such algorithms is change of the number of channels used for biomedical signal recording and processing according to the change of patient’s condition. To detect the exacerbation first symptoms by means of the patient's wearable computer, additional channels are activated for recording biomedical signals used to evaluate the expanded complex of diagnostically significant parameters of the disease and their integration when specifying the patient's condition. The system and intelligent monitoring algorithm is tested with the use of heart rate remote control and atrial fibrillation episode detection system. The testing results of the developed system and algorithm are discussed.

This chapter briefly overviews the digital signal processing with an outline of introduction and importance of biomedical signal processing. Biomedical signal processing plays an important role in analysis of human body. For this, bioelectric potential needs to be recorded digitally and processed through digital processing system. In general, all the real-world signals are analog in nature. Initially, these analog signals are converted into digital signals, and then digital filters and other signal processing techniques are applied over them. Same is also followed in case of biomedical signals. A brief overview of this conversion process is also given in this chapter. Mainly, this chapter explores about generation process, recording, signal processing and common problems of electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Real signals are also visualized that are taken from known datasets. This chapter provides a good base for any beginner in the field of biomedical signal processing.

Covering the latest cutting-edge techniques in biomedical signal processing while presenting a coherent treatment of various signal processing methods and applications, this second edition of Practical Biomedical Signal Analysis Using MATLAB® also offers practical guidance on which procedures are appropriate for a given task and different types of data. It begins by describing signal analysis techniques—including the newest and most advanced methods in the field—in an easy and accessible way, illustrating them with Live Script demos. MATLAB® routines are listed when available, and freely available software is discussed where appropriate. The book concludes by exploring the applications of the methods to a broad range of biomedical signals while highlighting common problems encountered in practice. These chapters have been updated throughout and include new sections on multiple channel analysis and connectivity measures, phase-amplitude analysis, functional near-infrared spectroscopy, fMRI (BOLD) signals, wearable devices, multimodal signal analysis, and brain-computer interfaces. By providing a unified overview of the field, this book explains how to integrate signal processing techniques in biomedical applications properly and explores how to avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods. It will be an excellent guide for graduate students studying biomedical engineering and practicing researchers in the field of biomedical signal analysis. Features: Fully updated throughout with new achievements, technologies, and methods and is supported with over 40 original MATLAB Live Scripts illustrating the discussed techniques, suitable for self-learning or as a supplement to college courses Provides a practical comparison of the advantages and disadvantages of different approaches in the context of various applications Applies the methods to a variety of signals, including electric, magnetic, acoustic, and optical Katarzyna J. Blinowska is a Professor emeritus at the University of Warsaw, Poland, where she was director of Graduate Studies in Biomedical Physics and head of the Department of Biomedical Physics. Currently, she is employed at the Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. She has been at the forefront in developing new advanced time-series methods for research and clinical applications. Jarosław Żygierewicz is a Professor at the University of Warsaw, Poland. His research focuses on developing methods for analyzing EEG and MEG signals, brain-computer interfaces, and applications of machine learning in signal processing and classification.

Biomedical signal acquisition has advanced greatly over the years, encompassing many different technologies. With the increasing performance of personal computers, PC-based signal processing systems are becoming an efficient and cost-effective way of acquiring and analyzing these signals. The advanced analysis techniques available on the PC are becoming invaluable to the practicing physician. This paper describes a PC-based signal acquisition, processing and analysis system using Labview, a graphics-based programming language for engineering applications.

Background and ObjectiveMachine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre‐trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal.MethodsThe DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding.ResultsWe developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant. The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre‐processing and post‐processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service.ConclusionThe DeepPlayer plugin allows running deep learning models easily and interactively. Therefore, experts and non‐AI experts alike can explore and apply deep learning models for (biomedical) signal processing. Its ease of use and interactivity might also contribute to a better understanding and acceptance of AI methods in biomedicine.

This paper proposes a new method named as impedance steering to improve the frequency response of the AC coupled instrumentation amplifier (IA) which is widely used for processing of biomedical (BM) signals. The method makes use of steering a high impedance in chain of resistors used at the input of IA with balanced AC coupling to achieve very high impedance during amplification of the input voltage. This results in uniform frequency response even at the lower frequencies which is major concern for processing of BM signals. This impedance steering technique can be used at input of any IA to construct a standalone amplification system for BM signals. However, in many applications, the output of IA is digitized using high resolution A/D conversion. A new method named a synchronized sampling and A/D conversion has been proposed in this paper for such applications. This method makes use of sampling and digitization of the signal which is synchronized with the process of steering the impedances to obtain high performance AC coupled BM signal processing system. The circuit has been designed and simulated using NI Multisim software, version 12. The results obtained using this method confirms the efficacy of the method for obtaining high performance signal processing required for BM signal applications. Though the system is preliminary designed for BM application, it can be used for wide range of signal processing.

A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification.

High-dimensional systems are increasingly common in fields such as remote sensing, communications, and biomedical signal processing, where data typically comprises multivariate signals with interdependent components. These systems often suffer from noise contamination, which can significantly reduce the fidelity of signal interpretation. Traditional noise-reduction techniques, such as Wiener filtering or wavelet-based methods, frequently fail to perform optimally when applied to high-dimensional data due to challenges associated with high variance, noise correlation, and large computational demands [1-5]. In this article, we review state-of-the-art signal-processing algorithms for noise reduction specifically designed for high-dimensional systems. We focus on dimensionality reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), and kernel-based approaches, which have shown promise in improving signal fidelity while reducing noise [6, 7]. Furthermore, we introduce a novel hybrid algorithm that combines dimensionality reduction with adaptive filtering, hypothesizing that this integration will yield better results compared to conventional methods that rely solely on adaptive filtering. The objective is to demonstrate that by reducing dimensionality before applying adaptive filtering, the SNR (signal-to-noise ratio) can be significantly improved. Simulation results on high-dimensional datasets in remote sensing and biomedical signal applications confirm that the proposed hybrid approach outperforms baseline techniques, showing higher robustness and efficiency in both noise suppression and computational load [8-11]. This research highlights key contributions in the field, focusing on the advantages of incorporating dimensionality reduction as a preprocessing stage in high-dimensional signal processing [12-15]. Future work may involve fine-tuning these algorithms for real-time applications in various domains.

Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.

This study presents the design and validation of an open-source framework for biomedical signal acquisition and interoperable data exchange based on the Health Level Seven—Fast Healthcare Interoperability Resources (HL7 FHIR) standard. The proposed system enables secure, wireless transmission of physiological data from distributed sensing nodes toward a locally hosted monitoring platform. The hardware architecture integrates ESP32-WROOM-32 microcontrollers for multi-parameter acquisition, the MQTT protocol for low-latency communication, and a Home Assistant (Nabu Casa, San Diego, CA, USA)–InfluxDB (InfluxData, San Francisco, CA, USA)–Grafana (Grafana Labs, New York, NY, USA) stack for real-time visualization. The novelty of this work lies in the full-stack implementation of HL7 FHIR Observations within a reproducible, open-source environment, ensuring semantic interoperability without reliance on proprietary middleware or cloud services. A case study involving multi-sensor acquisition of electrocardiographic (ECG), photoplethysmographic (PPG), temperature, and oxygen saturation signals was conducted to evaluate system performance. Validation results confirmed consistent end-to-end data flow, sub-second latency, zero packet loss, and accurate semantic preservation across all processing stages. These findings demonstrate the feasibility of implementing standardized, open, and scalable biomedical Internet of Medical Things (IoMT) systems using non-proprietary components. The proposed framework provides a reproducible foundation for future telemedicine and continuous patient-monitoring applications, aligning with FAIR data principles and the ongoing digital transformation of healthcare.

Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model baseof the CDSS.

Over the last years, much effort has been made toward developing computerized methods to detect seizures in patients by scientists. This paper is to present a Field Programmable Gate Array (FPGA)-based Fir Filter Design for Biomedical signal processing. The Electroencephalography (EEG) has been the most dependable tool used for biomedical signal processing. The digital filter is used to filter discrete time signals with the ability to modify the frequency response of the filter at any time and it is used many application such as data compression, biomedical signal processing, communication receivers, etc. The Finite Impulse Response (FIR) filter has many advantages on signal processing such that it has a simple structure, stationary response and adaptivity with embedded microprocessors. FIR filter is proposed due to facilitate structural characteristic and design properties on filtering EEG signal. The system has a computer where the design can be programmed and simulated on Xilinx FPGA ISE editor. The focus of this study is to compute FIR filter coefficients on MATLAB Filter Design Toolbox and then transfer them to embedded design on FPGA to filter EEG signal.

First published in 1986: The presentation of the material in the book follows the flow of events of the general signal processing system. After the signal has been acquired, some manipulations are applied in order to enhance the relevant information present in the signal. Simple, Optimal, and adaptive filtering are examples of such manipulations. The detection of wavelets is of importance in biomedical signals; they can be detected from the enhanced signal by several methods. The signal very often contains redundancies. When effective storing, transmission, or automatic classification are required, these redundancies have to be extracted.