Biomedical Data Acquisition Research Articles

A 3D-Printed Piezoelectric Microdevice for Human Energy Harvesting for Wearable Biosensors.

The human body is a source of multiple types of energy, such as mechanical, thermal and biochemical, which can be scavenged through appropriate technological means. Mechanical vibrations originating from contraction and expansion of the radial artery represent a reliable source of displacement to be picked up and exploited by a harvester. The continuous monitoring of physiological biomarkers is an essential part of the timely and accurate diagnosis of a disease with subsequent medical treatment, and wearable biosensors are increasingly utilized for biomedical data acquisition of important biomarkers. However, they rely on batteries and their replacement introduces a discontinuity in measured signals, which could be critical for the patients and also causes discomfort. In the present work, the research into a novel 3D-printed wearable energy harvesting platform for scavenging energy from arterial pulsations via a piezoelectric material is described. An elastic thermoplastic polyurethane (TPU) film, which forms an air chamber between the skin and the piezoelectric disc electrode, was introduced to provide better adsorption to the skin, prevent damage to the piezoelectric disc and electrically isolate components in the platform from the human body. Computational fluid dynamics in the framework of COMSOL Multiphysics 6.1 software was employed to perform a series of coupled time-varying simulations of the interaction among a number of associated physical phenomena. The mathematical model of the harvester was investigated computationally, and quantification of the output energy and power parameters was used for comparisons. A prototype wearable platform enclosure was designed and manufactured using fused filament fabrication (FFF). The influence of the piezoelectric disc material and its diameter on the electrical output were studied and various geometrical parameters of the enclosure and the TPU film were optimized based on theoretical and empirical data. Physiological data, such as interdependency between the harvester skin fit and voltage output, were obtained.

Feasibility of using wearable technology to monitor agitation and sleep in patients with Alzheimer’s dementia: Lessons learned

AbstractBackgroundIndividuals with Alzheimer’s dementia (AD) often go through a period of significant behavioral and psychological symptoms including agitation. This can lead to impaired daily functioning, prolongation of hospitalization, early institutionalization, and higher mortality. The integration of biomedical data acquisition systems and information technology enables continuous real‐time monitoring of physiological data in daily life.MethodThis feasibility study is part of a clinical trial that examines the effects of THC‐free CBD oil on agitation in patients with AD (NCT04436081), involves caregiver/care‐recipient dyads and the use of technology to capture agitation during a 14‐week period. Participants are given 2 Fitbit versa 2 (each includes a 3‐axis accelerometer and an optical heart rate monitor), a smartphone, a tablet, and a digital timer. Participants’ Fitbit are paired with their smartphone, while the caregivers’ FitBit is paired with their tablet. Participants include males/females over 50 years old, with the diagnosis of AD, a MMSE score ≤28 & ≥4, and agitation measured with an NPI agitation/aggression subscore >3. Each dyad completes 24 forms/questionnaires throughout the study electronically via provided apps. Caregivers are instructed to send a real time electronic alert when agitation episodes happen.ResultSo far, we have enrolled 26 dyads. For this report we have collected data on 22 participants with 140 million data points for limb movement and heart rate, and 969 nights of sleep data records. Overall compliance with wearable use was 77% of the time, with 82% in 11 participants, 65% in 8 participants and 32% in 3 participants. Compliance with electronic form/questionnaire completion was 87%. Electronic alert for agitation episodes were recorded by the caregivers only 50% of time.ConclusionThis study explores the feasibility and acceptability of using technology (wearables, electronic form data completion and submission, sleep, and real time agitation episode collection) for a 16‐week period in AD patients with agitation and their caregivers. We found high acceptability of wearing the Fitbit by AD patients with agitation. The primary issues interfering with data collection and device compliance were device battery life and memory, remembering to wear the device after charging, and technical fault in capturing the data.

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique.

Lung cancer has the highest death rate of any other cancer in the world. Detecting lung cancer early can increase a patient's survival rate. The corresponding work presents the method for improving the computer-aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the first step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identified and segmented with an optimal threshold value and rule-based trimming. It also selects 2D and 3D features from the candidate segmented node. In the final step, the selected features are used to train the SVM and classify the nodes and classify the non-nodes. To assess the performance of the proposed framework, experiments were performed on the LIDC data set. As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 FP per scan with a sensitivity of 95%.

Interactive Cardio System for Healthcare Improvement.

The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people's quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered.

Enhanced dataset synthesis using conditional generative adversarial networks.

Biomedical data acquisition, and reaching sufficient samples of participants are difficult and time ans effort consuming processes. On the other hand, the success rates of computer aided diagnosis (CAD) algorithms are sample and feature space depended. In this paper, conditional generative adversarial network (CGAN) based enhanced feature generation is proposed to synthesize large sample datasets having higher class separability. Twenty five percent of five medical datasets are used to train CGAN, and the synthetic datasets with any sample size are evaluated and compared to originals. Thus, new datasets can be generated with the help of the CGAN model and lower sample collection. It helps physicians decreasing sample collection processes, and it increases accuracy rates of the CAD systems using generated enhanced data with enhanced feature vectors. The synthesized datasets are classified using nearest neighbor, radial basis function support vector machine and artificial neural network to analyze the effectiveness of the proposed CGAN model.

Acquisition and Processing of Biomedical Data for Outpatient Care in Rural Areas

Abstract Acquisition of medical data such as blood pressure, ECG, pulse, and other values is currently often carried out in doctor’s offices by physicians or medical staff. This requires valuable time of patients as well as of practices’ personel and can deliver specific data only of the time of the visit. In this paper we describe a different approach. Patients measure their biomedical vital data at home. Therefore a technical infrastructure together with a workflow were developed and applied within a current project with patients in a rural area. Vital data is transferred via smartphone apps to a cloud environment where doctors can easily access and assess the data.

Internet of things-assisted architecture for QRS complex detection in real time

This paper presents the development of an Internet of Things-assisted architecture for QRS complex detection in an electrocardiogram regardless of the age and physiological characteristics of the patient. Detection of the QRS complex is affected by the abnormalities and quality in electrocardiogram recordings; the proposed method can detect QRS complex despite these challenges. Electrocardiogram continuous signal acquisition is performed with the BITalino biomedical data acquisition card. Electrocardiogram signals typically suffer from (a) premature atrial complexes, (b) premature ventricular complexes, (c) low signal-to-noise ratio, (d) right bundle branch blocks, (e) left bundle branch blocks, and (f) non-stationary effects. Interestingly, the signal processing is implemented by means of a bandpass filter, followed by a numerical derivative. Next, the Hilbert transform and the adaptive threshold technique are implemented to detect the QRS complex. Tests are performed to evaluate the Internet of Things-assisted architecture using the obtained signal in real time. Results, and the simplicity of the architecture, demonstrate that it is suitable for wearable, portable, and battery-operated electrocardiogram acquisition card.

IoT based Biomedical Intelligent System in Agriculture

Farming is one of the conventional occupations in Goa. The predominant livelihood relies upon farming and agriculture. However, the principal hassle faced by farmers right here is deteriorated health due to untimely knowing in their health status when working in farming or agricultural field. Hence, in this paper, IoT based biomedical real-time data acquisition system, as well as communication of detected health parameters using wireless technology, is proposed. The received parameters are then examined and analysed by officials of rural health centre for providing necessary medical help for farmers. Same will also be displayed on IoT based cloud server. The analysis of received health parameters is executed using soft computing technique called fuzzy logic. The predominant concern for existing Wi-Fi technology is weak and interrupted wireless transmission of data in terms of speed, bandwidth and availability of lesser frequency bands. Hence, subsequently, we have proposed a high performing multi-frequency band microstrip square patch antenna design at a resonance frequency of 2.4 GHz for fast and efficient wireless communication of data and also applicable for various advanced wireless technology such as Wi-Fi 5, Wi-Fi 6 and beyond. The proposed antenna offers effective S-parameters, desired standing wave ratio, good bandwidth, and a good amount of radiated power at multiple frequencies in the frequency band of 1 GHz- 10 GHz.

Cardiac Arrhythmia Prediction by Adaptive Analysis via Bluetooth

In this work, the development of a data acquisition system for adaptive monitoring based on a dynamic quadratic neural unit is presented. Acquisition of the continuous signal is achieved with the BITalino biomedical data acquisition card. The system is trained sample-by-sample with a real time recurrent learning method. Then, possible cardiac arrhythmia is predicted by implementing the adaptive monitoring in real time to recognize patterns that predict cardiac arrhythmia up to 1 second in advance. For the evaluation of the interface, tests are performed using the obtained signal in real time

Software architecture optimization of mobile biomedical sensor-based tools providing analytical services for disease diagnostics and assistance

The development of highly specialized mobile applications and systems has risen for several years, as we observe rapid deployment of innovative biomedical sensors and wearable technologies. The experiences gathered over 10 years of mobile medical software development, provide practical recommendations for architectural concepts utilized in analytical health-based services. Constructed systems and mobile applications in majority of cases utilize biomedical signals to identify health state of a patient, as well as to evaluate or estimate the intensity of disease symptoms. Based on these experiences, this paper proposes architectural concepts, for both mobile and web-based components aimed at acquisition and processing of biomedical data in large scale medical systems dedicated for monitoring of patients. The major assumptions for this research included utilization of wearable and mobile technologies, supporting maximum number of wearable diagnostic technologies including inertial and biomedical data streams. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Optimized architecture variants contain several schemes for data processing, moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile application and further to server services. The analysis of delays during data transmission and processing, provided architecture variants’ pros and cons but most of captured recommendations for applicability of such mechanisms in the domains of medical, military and fitness. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Analytical processing of inertial or biomedical sensor data requires filtering, time-window processing, aggregation, application of specific algorithmic transformations etc. Combination of several data sources and correlation of biomedical parameters between each other and specific patient’s health state, extend predefined ordinarily used event triggering rules. This work provides construction details of wearable-based mobile systems with specialized aimed at health state identification and monitoring. Undertaken construction decisions have been confirmed and justified based on functional and stress tests of system components. The first deployment attempt of designed architecture and its implementation has been aimed at remote, mobile monitoring of elderly people, and preconfigured for crucial health event recognition – fainting, stroke, cardiac arrest, seizures, some classes of neurological disorders and derivatives of such conditions.

National Instruments LabVIEW Biomedical Toolkit for Measuring Heart Beat Rate and ECG LEAD II Features

Electrocardiogram (ECG) is undoubtedly the gold method for assessing heart activities. However, the ECG machine for measuring ECG is mostly only available in health care center especially in the third-world countries. Therefore, this research proposed to utilize the product of National Instruments i.e. LabVIEW Biomedical Toolkit and data acquisition device as an alternative approach to acquire ECG features and heart beat rate (HBR). As for performances verification, the NI-LabVIEW instruments are tested simultaneously with two types of ECG machine which are Philips PageWriter TC 30 and KenZ ECG 108. The results indicated that the ECG features and HBR obtained from NI-LABVIEW instruments are agreed well with the measurements attained from both of the ECG machines. Hence, this study proved that NI-LabVIEW instruments are highly potent for measuring ECG features and HBR. Moreover, the computerized NI-LabVIEW instruments with the self-interpretation function can always outperform most of the conventional ECG machines that are still required cardiologists for manually interpreting the results printed on the ECG paper.

An IoT framework for Bio-medical sensor data acquisition and machine learning for early detection

An IoT framework for Bio-medical sensor data acquisition and machine learning for early detection

The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine

Background:Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.Objective:This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.Conclusion:Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability

BackgroundNetwork controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.ResultWe develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user’s query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php.ConclusionThe pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

Architectural concepts for managing biomedical sensor data utilised for medical diagnosis and patient remote care.

This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.

Medical decision support system for extremely imbalanced datasets

Advanced biomedical instruments and data acquisition techniques generate large amount of physiological data. For accurate diagnosis of related pathology, it has become necessary to develop new methods for analyzing and understanding this data. Clinical decision support systems are designed to provide real time guidance to healthcare experts. These are evolving as an alternate strategy to increase the exactness of diagnostic testing. Generalization ability of these systems is governed by the characteristics of dataset used during its development. It is observed that sub pathologies have a much varied ratio of occurrence in the population, making the dataset extremely imbalanced. This problem can be resolved at both levels i.e. at data level as well as algorithmic level. This work proposes a synthetic sampling technique to balance dataset along with Modified Particle Swarm Optimization (M-PSO) technique. A comparative study of multiclass support vector machine (SVM) classifier optimization algorithm based on grid selection (GSVM), hybrid feature selection (SVMFS), genetic algorithm (GA) and M-PSO is presented in this work. Empirical analysis of five machine learning algorithms demonstrate that M-PSO statistically outperforms the others.

A novel low-noise fully differential CMOS instrumentation amplifier with 1.88 noise efficiency factor for biomedical and sensor applications

In this paper, we present a novel instrumentation amplifier (INA) topology for mobile bio-medical data acquisition platforms. The proposed INA features a PMOS-NMOS complimentary transistor input pair biased in sub-threshold region to effectively boost the transconductance of the input pair and reduce the input referred noise. There are two embedded common-mode feedback circuits which help to establish the common-mode voltages without consuming extra power. One is at the output of the first stage (i.e. at the drain connection of the complimentary transistors) and the other at the final output stage. The INA provides a measured gain of 40dB and a bandwidth of 11kHz. The measured integrated noise is 0.78μVrms (50mHz to 11kHz) with measured CMRR of greater than 100dB. The proposed amplifier is versatile and hence capable of conditioning various bio-potential signals like Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG) and Electrooculogram (EOG) as well as signals produced from sensors. A prototype chip has been fabricated in UMC 180nm mixed-mode CMOS technology operating at 1.8V power supply and occupying an area of 415×230μm2. The simulation and experimentally measured results are presented in this paper. NEF (noise efficiency factor) of 1.88 is obtained from measurement results. Hence the amplifier features for the first time a very low NEF (1.88) and high CMRR (>100dB). A compact ExG conditioning and acquisition system has been developed and used to measure ECG, EEG, EMG and EOG signals from human subjects by deploying the proposed INA.

Integrated e-Healthcare System for Elderly Support

The fast rise of the population aging verified in the last decades brings new challenges to the modern societies. Most elderly persons have the usual problems related to the old age, like health chronic problems and sensory and cognitive impairments. Therefore, it becomes essential to ensure the quality of life, safety and well-being to all elderly persons. The evolution of the sensors technology, low-power microelectronics and wireless communication standards allows that the gerontechnology be increasingly available and present in our society. This paper presents an integrated e-healthcare system for elderly support, which allows monitoring the biomedical parameters of a person in real time, anywhere and in any situation without interfering with its daily routines. The developed system comprises a personal biomedical data acquisition subsystem and an information storage center. The developed sensorial devices are responsible for acquiring and transmit wirelessly the biomedical signals to a smartphone or tablet. The collected information can also be saved in a storage center, where it can be managed and maintained. The medical data are accessible to the responsible entities for creating the medical history of the elderly persons to ensure a well-founded diagnosis. The high processing capacity of the developed electronic system enables the implementation of advanced algorithms for detection of health problems in order to ensure the safety and well-being of the elderly throughout the day. The medical assistance platform also provides to the elderlies telemedicine consultations in the comfort of their home if the videoconferencing service of the platform is used.

Self-Dithering Technique for High-Resolution SAR ADC Design

In this brief, a high-resolution successive-approximation-register analog-to-digital-conversion architecture for biomedical data acquisition is proposed. A filtered least-significant-bit segment is employed as a dither to improve the resolution. Theoretical analysis and behavioral simulations show that the error of a most-significant-bit segment can be converted into shaped noise if the input signal is sufficiently small. The proposed self-dithering technique can be used, together with averaging, to improve the signal-to-noise ratio and the differential nonlinearity (DNL) performance. The performance improvement is similar to that of a conventional nonsubtractive scheme using a uniform deterministic dither but with simplified hardware and reduced computation complexity.

Field programmable gate arrays implementation of Dual Tree Complex Wavelet Transform.

Due to the inherent time-varying characteristics of physiological systems, most biomedical signals (BSs) are expected to have non-stationary character. Therefore, any appropriate analysis method for dealing with BSs should exhibit adjustable time-frequency (TF) resolution. The wavelet transform (WT) provides a TF representation of signals, which has good frequency resolution at low frequencies and good time resolution at high frequencies, resulting in an optimized TF resolution. Discrete wavelet transform (DWT), which is used in various medical signal processing applications such as denoising and feature extraction, is a fast and discretized algorithm for classical WT. However, the DWT has some very important drawbacks such as aliasing, lack of directionality, and shift-variance. To overcome these drawbacks, a new improved discrete transform named as Dual Tree Complex Wavelet Transform (DTCWT) can be used. Nowadays, with the improvements in embedded system technology, portable real-time medical devices are frequently used for rapid diagnosis in patients. In this study, in order to implement DTCWT algorithm in FPGAs, which can be used as real-time feature extraction or denoising operator for biomedical signals, a novel hardware architecture is proposed. In proposed architecture, DTCWT is implemented with only one adder and one multiplier. Additionally, considering the multi-channel outputs of biomedical data acquisition systems, this architecture is capable of running N channels in parallel.