Development of a low-cost, compact, wireless, 16 - channel biopotential data acquisition, signal conditioning and arbitrary waveform stimulator.

The health and fitness of the human body rely heavily on physiological parameters. These parameters can be measured using various tools such as ECG, EMG, EEG, EOG, among others, to obtain real-time physiological data. Analysing the bio-signals obtained from these measurements can provide valuable information that can be used to improve health-care in terms of observation, diagnosis, and treatment. In bio-signal pattern recognition applications, more channels provide multiple information simultaneously. Different biosignal acquisition devices are available in the market, most of which are designed for specific signals like ECG, EMG, EEG etc The gain of the amplifiers and frequency of the filters are designed as per the targeted signals; due to which one device cannot be used for other signals. Also, most of the systems are wired system which is not comfortable for animal studies. In this paper, a low-cost, compact, wireless, 16 channel biopotential data acquisition system with integrated electrical stimulator is designed and implemented. There are several novel and flexible design approaches were incorporated in the proposed design like (1) It has user selectable digital filter in each channel based on the signal frequencies like ECG, EMG, EEG, EOG. The same system will be used to acquire different signals simultaneously. (2) It has variable gain with a configurable analog bandpass filter. (3) It can acquire signals from 4 patients simultaneously. (4) The system is capable to acquire signal from both two-electrode as well as three-electrode configurations. (5) It has integrated stimulator with trapezoidal, charge-balanced, biphasic stimulus output with near zero DC level and user selectable pulse duration or frequency of the stimulus. The developed system has the ability to acquire and transmit data wirelessly in real-time at a high transfer rate. To validate the performance of the system, tests were conducted on the acquired signals using a simulator.

Honeybee stinger-based biopsy needle and influence of the barbs on needle forces during insertion/extraction into the iliac crest: A multilayer finite element approach.

Bone marrow biopsy (BMB) needles are frequently used in medical procedures, including extracting biological tissue to identify specific lesions or abnormalities discovered during a medical examination or a radiological scan. The forces applied by the needle during the cutting operation significantly impact the sample quality. Excessive needle insertion force and possible deflection might cause tissue damage, compromising the integrity of the biopsy specimen. The present study aims at proposing a revolutionary bioinspired needle design that will be utilized during the BMB procedure. A non-linear finite element method (FEM) has been used to analyze the insertion/extraction mechanisms of the honeybee-inspired biopsy needle with barbs into/from the human skin-bone domain (i.e., iliac crest model). It can be seen from the results of the FEM analysis that stresses are concentrated around the bioinspired biopsy needle tip and barbs during the needle insertion process. Also, these needles reduce the insertion force and reduce the tip deflection. The insertion force in the current study has been reduced by 8.6% for bone tissue and 22.66% for skin tissue layers. Similarly, the extraction force has been reduced by an average of 57.54%. Additionally, it has been observed that the needle-tip deflection got reduced from 10.44 mm for a plain bevel needle to 6.3 mm for a barbed biopsy bevel needle. According to the research findings, the proposed bioinspired barbed biopsy needle design could be utilized to create and produce novel biopsy needles for successful and minimally invasive piercing operations.

A prospective survey on trephine biopsy of bone and bone marrow: an experience with 274 Indian patients' biopsies.

Trephine bone marrow biopsy is an effective technique for diagnosing hematological malignancies in patients of different ages. During trephine biopsy, bone marrow cores are obtained for detailed morphological evaluation to look for any abnormality and arrive at a diagnosis. The primary goal of this work is to perform a survey on Indian patients of various ages for the trephine bone marrow biopsy process. In the present study, data related to 274 trephine biopsy samples from 300 patients were acquired at the Post Graduate Institute of Medical Education and Research (PGIMER) in Chandigarh, India. Pain was found to be the sole major procedure-related complication, and patients reported no/less pain in 41 BMB (14.96%) patients, moderate pain in 82 (29.92%) cases, and unbearable pain in 151 (55.1%) BMB cases. In addition, the patients were evaluated by the authors and hematologist as non-anxious for the procedure in 34 (12.4%), anxious in 92 (33.57%), and very/highly anxious in 148 (56%) cases. The bone texture of the patients significantly affected the needle bending, number of repetitions required, and size of the bone marrow sample. This demonstrates the need for improvement in the biopsy procedure. To this end, a survey was conducted to assess the numerous difficulties and diagnostic outcomes throughout the trephine biopsy process.

Techniques for the detection and management of freezing of gait in Parkinson's disease - A systematic review and future perspectives.

Freezing of Gait (FoG) is one of the most critical debilitating motor symptoms of advanced Parkinson's disease (PD) with a higher rate of occurrence in aged people. PD affects the cardinal motor functioning and leads to non-motor symptoms, including cognitive and neurobehavioral abnormalities, autonomic dysfunctions and sleep disorders. Since its pathogenesis is complex and unclear yet, this paper targets the studies done on the pathophysiology and epidemiology of FoG in PD. Gait disorder and cardinal features vary from festination (involuntary hurrying in walking) to freezing of gait (breakdown of repetitive movement of steps despite the intention to walk) in patients. Hence, it is difficult to assess the FoG in clinical trials. Therefore, the current research emphasizes wearable sensor-based systems over pharmacology and surgical methods.•This paper presents a technological review of various techniques used for the assessment of FoG with a comprehensive comparison.•Researchers are aiming at the development of wireless sensor-based assistive devices to (a) predict the FoG episode in a different environment, (b) acquire the long-term data for real-time analysis, and (c) cue the FoG patients.•We summarize the work done till now and future research directions needed for a suitable cueing mechanism to overcome FoG.

Finite element simulation of multilayer model to simulate fine needle insertion mechanism into iliac crest for bone marrow biopsy.

The main aim of this work is to use a finite element technique (FEM) to gain understanding about the bone marrow biopsy (BMB) needle insertion process and needle-tissue interactions in the human iliac crest. A multi-layer iliac crest model consists of stratum corneum, dermis, epidermis, hypodermis, cortical, and cancellous bone has been established. This paper proposes a FE model that examines all phases of tissue deformation, including puncture, cutting, needle-tissue interaction, and various stress-strain values for BMB needle during interaction. The results explain the needle-tissue interface and show the potential of this technique to estimate bone damage and tissue deformation for multiple needle dimensions, coefficient of friction, and penetration speeds. The insertion and extraction force of conical-shaped needles in the multi-layered iliac crest model decreased by 18.92% and 37.5%, respectively, as the needle diameter reduced from 11 G to 20 G. It has also been found that the significant insertion motion raises the deformation of the tissue due to the augmented frictional forces but reduces the strain perpendicular to the penetration direction closer to the needle tip. The simulation outcomes are helpful for the optimal design of fine biopsy needles used to perform the bone marrow biopsies.

Automatic Pasteurized Formula Milk Preparation Machine with Automatic Sterilized Containers.

Children are the future of our generation, so reducing child mortality is very critical in developing countries. There are lots of asserting factors of child mortality but malnutrition is one of the prominent factors. Medically, it has been proven that breastfeeding is one of the sources of nutrients and it is being always appreciated to have mothers' milk to a child in the early days. However, with the increasing participation of women in the workforce, the child care load on breastfeeding mothers is very high. This forces many of them to take long career breaks. Here, in this paper, we have an automatic formula milk dispensing unit that will be used in Neonatal Intensive Care Units (NICUs) and for breastfeeding mothers at home. The device has inbuilt sterilization and pasteurization units that would maintain the overall hygiene and sterilization of baby milk bottles. Currently, the device has a few buttons through which we can control the functionality of the device.

Concept, hardware development, and clinical trials of a Galinstan based Mercury free sphygmomanometer: Merkfree.

The aim of this work is to develop Merkfree-a mercury-free sphygmomanometer that looks, feels, and operates just like a traditional mercury sphygmomanometer (MS). For this we use Galinstan as a substitute for mercury, which is a non-toxic alloy of Gallium, Indium and Tin. Galinstan is nearly half as dense as mercury and sticks to class. To work with the lower density, we designed an enclosure and scale that is nearly double the length of MS. The issue of stickiness with glass was resolved by maintaining a small meniscus of a reducing agent in the measuring tube and tank of Merkfree. Clinical trials to validate the accuracy of Merkfree against MS and oscillometric sphygmomanometer (OS) were conducted over 252 patients. The results show a good correlation of the systolic and diastolic BP measurements from Merkfree with respect to MS and the OS. The mean absolute percentage error is less than 10% for both SBP and DBP. We also found that Merkfree has lower rounding-off errors compared to MS. Merkfree can be a viable alternative to mercury sphygmomanometer that can help achieve the goal of WHO in eliminating mercury from healthcare, while simultaneously making sure that gold standard technique of sphygmomanometry continues to be available to the clinicians.

Improvising limitations of DNN based ultrasound image reconstruction.

Ultrasound modalities are cost-effective and radiation-free technology for real-time medical imaging. These modalities require image reconstruction to obtain the actual ultrasound images from ultrasound raw data. The ultrasound raw data is obtained in the form of echo after scanning an imaging plane through ultrasound waves. The most commonly used image reconstruction beamforming technique is Delay and Sum (DAS). Other sophisticated beamforming techniques are Delay Multiply and Sum (DMAS) and Minimum Variance Distortionless Response (MVDR). DAS has limited image quality, and the employment of sophisticated techniques increases the computational complexity and computational time with improvement in image quality. To overcome these problems, various DNN (Deep Neural Networks) based techniques have been proposed which can reconstruct ultrasound images directly from ultrasound raw data. But DNN implementation has two limitations: accuracy of reconstruction and generalizability of the model. To overcome these limitations, we are proposing methodologies with a DNN model which was able to reduce these limitations. Firstly, we generated the datasets which include multiple shapes such as line, circle, ellipse, and parabola. After that, we have implemented a CNN-DNN (Convolution Neural Network and Deep Neural Network) hybrid model which has significantly improved computational time as well as image quality. We have trained our model with different sets of data to validate the reconstruction of the image matrix. We achieved a significant improvement in computational time of around 100 times (from around 0.6 s to 0.0059 s) as compared to DAS beamforming technique. At the same time, we also achieved a significant improvement in image quality with 37.19 dB average and 41.37 dB maximum improved Peak Signal to Noise Ratio (PSNR), and 87.41% average and 95% maximum Structural Similarity Index Matrix (SSIM) value. We also achieved generalizability and precise image reconstruction by using the proposed model.

A review on artificial pancreas and regenerative medicine used in the management of Type 1 diabetes mellitus.

Diabetes mellitus is one of the fastest-growing lifestyle disorders in the world. While numerous regimes have been developed to manage diabetes, there continue to be high numbers of diabetes-related deaths worldwide. The review gives a brief introduction to the pathology and aetiology of the disorder, different solutions developed over time with their advantages and disadvantages, and highlights the technological components and challenges of the latest technologies: artificial pancreas and regenerative medicine. The study is restricted to a set of high-quality publications from the last decade.

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit.

Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963-0.972), heart failure AUC of 0.838 (CI: 0.825-0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821-0.842), pulmonary embolism AUC of 0.802 (CI: 0.764-0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499-2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound.

Arterial stiffness (AS) of the carotid artery is an early marker of stratifying cardiovascular disease risk. This article aims to improve the performance of ARTSENS, a noninvasive A-mode ultrasound-based device for measuring AS. The primary objective of ARTSENS is to enable the measurement of elastic modulus using A-Mode ultrasound and blood pressure. As this device is image-free, there is a need to automate: 1) carotid detection; 2) wall localization; and 3) inner lumen diameter measurement. This has been performed using conventional signal processing methods in some of the earlier works in this domain. In this article, deep neural network (DNN) models are employed to perform the above three tasks. The DNNs were trained over data acquired from 82 subjects at two different medical centers. Ground-truth labeling was performed by a trained operator using corresponding measurements from the state-of-the-art Aloka e-Tracking system. All three DNN models had significantly lower errors compared to earlier signal processing methods and could perform their measurements using a single A-Mode frame. Using the DNNs, two different machine learning pipelines have been proposed here to measure the elastic modulus; the best among them could achieve an error of 9.3% with the Pearson correlation coefficient of 0.94 ( ). The models were tested on Raspberry Pi and Jetson Nano single board computers to demonstrate real-time processing on low computational resources.

Recent advances in low-cost, portable automated resuscitator systems to fight COVID-19.

World is fighting one of its greatest battle against COVID-19 (a highly infectious disease), leading to death of hundreds of thousands of people around the world, with severe patients requiring artificial breathing. To overcome the shortage of ventilators in medical infrastructure, various low-cost, easy to assemble, portable ventilators have been proposed to fight the ongoing pandemic. These mechanical ventilators are made from components that are generally readily available worldwide. Such components are already associated with day-to-day gadgets or items and which do not require specialized manufacturing processes. Various designs have been proposed, focussing on meeting basic requirements for artificial ventilation to fight the ongoing pandemic. But some people are against the usage of these mechanical ventilators in real-life situations, owing to poor reliability and inability of these designs to meet certain clinical requirements. Each design has its own merits and demerits, which need to be addressed for proper designing. Therefore, this article aims to provide readers an overview of various design parameters that needs to be considered while designing portable ventilators, by systematic analysis from available pool of proposed designs. By going through existing literature, we have recognized multiple factors influencing device performance and how these factors need to be considered for efficient device operation.

A Device to Reduce Vasovagal Syncope in Blood Donors.

Vasovagal Syncope (VVS), or the transient loss of consciousness is the most widely recognized reason for syncope. (VVS), is a typical dysfunction of the autonomic nervous system. There are various factors which can influence the syncope. The major classification of the syncope are reflex(neurally mediated) syncope, syncope due to orthostatic hypertension, Cardiac syncope(cardiovascular). The vasovagal syncope is the part of reflex (neurally mediated)syncope, there are various cause of vasovagal reactions but in blood donation it is mediated due to the pooling of blood at calf muscles. Such near syncope incidence while donating the blood or after donation hampers the future motivation for blood donation of the donors. In this paper, we developed an electronic massager for calf muscles that can reduce the risk of VVS. It has a programmable circuit which can control the vacuum pump so that it can inflate and deflate the cuffs synergistically. The massager can relax the blood donor thereby reducing apprehension prior to blood donation and thus diverting from the trigger of Phlebotomy and improve peripheral blood circulation thereby improving venous return to the heart. This is expected to reduce the risk of VVS.

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm.

AIMS: This work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. METHODS AND RESULTS: We use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%. CONCLUSIONS: In conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.

Auscl-D: a mercury-free digital auscultatory sphygmomanometer.

More than quarter of world's population is consumed by hypertension, leading to premature death of thousands of people per year across the globe. One of the major reasons behind hypertension misdiagnosis is inaccurate blood pressure (BP) measurements, which can be attributed to various human or instrumentation errors. Currently used BP measuring sphygmomanometers, suffers from poor reliability, performance deterioration over time or are unable to meet environmental protection protocols. In this article, we propose a low-cost, highly portable, light-weight, easily manufacturable, battery operated, mercury free, auscultation based digital sphygmomanometer - Auscl-D, for easy and accurate BP measurements. The proposed device can be made from easily available components with cheap manufacturing processes available throughout globe, even in remote areas. The device demonstrated to have accuracy comparable to mercury sphygmomanometer, which is the gold-standard for BP measurements. The preliminary clinical trials were conducted at Dayanand Medical College & Hospital (DMCH) (Ludhiana, India), to compare the performance of proposed device with commonly used aneroid sphygmomanometers employing the auscultatory method and validated oscillometric sphygmomanometers from MicroLife. The test results show good agreement for systolic and diastolic BP measurements taken using Auscl-D device compared to the aneroid and oscillometric types. This shows the potential of proposed design to serve as low-cost, highly portable replacement for conventionally used sphygmomanometers, without the toxicity and reliability issues.

A survey to gauge confidence of Indian clinicians on three primary devices for blood pressure measurement.

PURPOSE: As per its commitment at Minamata convention, and in line with other developed economies, the Indian government is set to ban the use of mercury sphygmomanometers by end of the year 2020. However, the Mercury sphygmomanometer is still widely used by clinicians in India. We conducted a survey to gauge the confidence of Indian clinicians on three primary devices of blood pressure (BP) measurement - mercury sphygmomanometer, aneroid sphygmomanometer and automatic digital BP monitor. MATERIALS AND METHODS: We conducted an anonymous online survey through various clinician forums asking questions related to accuracy, reliability and expectations from BP monitors. RESULTS: A total of 139 responses were received from clinicians across specialties. The results show that more than 80% of clinicians believe that mercury sphygmomanometers are the most accurate and nearly 50% find it most reliable. For most respondents, accuracy is the most important parameter and convenience of use and portability are secondary considerations. If a mercury-free sphygmomanometer is offered with the same accuracy and reliability, 88% of respondents said they are willing to buy it. CONCLUSIONS: Mercury sphygmomanometer is still perceived favorably over other non-mercury alternatives by most Indian clinicians. Validated oscillometric devices should be promoted to bring about change in the perspectives of clinicians towards adopting non-mercury alternatives of BP measurement in India.

Negative pressure aerosol containment box: An innovation to reduce COVID-19 infection risk in healthcare workers.

Healthcare workers (HCW's) are at increased risk of corona virus disease (COVID-19) infection during aerosol generating activities. The aerosol box has been used during intubation and extubation to prevent transmission of infection to HCWs. Isolation room with negative pressure has been advocated for COVID-19 patients. The described containment box has been designed to be useful in COVID intensive care unit (ICU) as a multipurpose box which is a cost effective and readily available resource. This innovation combines the containment box with negative pressure generation using central vacuum.

Reduction of false alarms in the intensive care unit using an optimized machine learning based approach.

This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric "score" from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.

Severe Manifestations of Chikungunya Fever in Children, India, 2016.

Chikungunya is a relatively benign disease, and a paucity of literature on severe manifestations in children exits. We describe a cohort of pediatric chikungunya fever patients in New Delhi, India, who had severe sepsis and septic shock, which can develop during the acute phase of illness.

A Novel Point-of-Care Smartphone Based System for Monitoring the Cardiac and Respiratory Systems.

Cardio-respiratory monitoring is one of the most demanding areas in the rapidly growing, mobile-device, based health care delivery. We developed a 12-lead smartphone-based electrocardiogram (ECG) acquisition and monitoring system (called "cvrPhone"), and an application to assess underlying ischemia, and estimate the respiration rate (RR) and tidal volume (TV) from analysis of electrocardiographic (ECG) signals only. During in-vivo swine studies (n = 6), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. Ischemic indices calculated from each lead showed statistically significant (p < 0.05) increase within 2 min of occlusion compared to baseline. Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 3) were preceded by significant (p < 0.05) increase of the ischemic index ~1-4 min prior to the onset of the tachy-arrhythmias. In order to assess the respiratory status during apnea, the mechanical ventilator was paused for up to 2 min during normal breathing. We observed that the RR and TV estimation algorithms detected apnea within 7.9 ± 1.1 sec and 5.5 ± 2.2 sec, respectively, while the estimated RR and TV values were 0 breaths/min and less than 100 ml, respectively. In conclusion, the cvrPhone can be used to detect myocardial ischemia and periods of respiratory apnea using a readily available mobile platform.