Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database.

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.

Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. "Invisible" wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from "Invisibles" due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and 0.86±0.61 beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40-140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience.

Seizure Detection: A Low Computational Effective Approach without Classification Methods.

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin.

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions.

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland-Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R2 = 0.86) and MSi (R2 = 0.97); the Bland-Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

Lung ultrasound compared with bedside chest radiography in a paediatric cardiac intensive care unit.

AIM: Postoperative recovery of children with heart disease is encumbered by pulmonary complications like pneumothorax (PNX), pleural effusion (PLE), interstitial oedema and pulmonary consolidation (PC). Recently, lung ultrasound (LUS) has become an important diagnostic tool for evaluation of pulmonary diseases in the paediatric context. LUS is accurate in diagnosing pleural and parenchymal diseases. The aim of this study was to evaluate the accuracy of LUS in the identification of PNX, PLE and PC in a paediatric population of patients with congenital heart disease after heart surgery. METHODS: Fifty-three patients aged 0-17 years who underwent cardiac surgery were evaluated in the postoperative period by chest X-ray (CXR) and LUS at the same time. The methods where compared for recognition of PNX, PLE and PC. RESULTS: LUS showed a good agreement for PNX and a moderate agreement for both PLE and PC. LUS also showed a significantly superior relative sensitivity than CXR for PC and PLE and a significantly inferior relative sensitivity for PNX. CONCLUSION: This study confirms that LUS has a sufficient agreement rate with the current clinical standard (CXR). Non-inferiority in diagnosis together with the easiness of bedside performance makes LUS a very attractive tool for the paediatric cardiac intensive care unit.

Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.

As a definition, Human-Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs' complexity, so their usefulness should be carefully evaluated for the specific application.

Respiration Monitoring via Forcecardiography Sensors.

In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients' discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R2 = 0.98) vs. 0.98, 0.11 s (R2 = 0.88), Bland-Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.

Continuous Vital Monitoring During Sleep and Light Activity Using Carbon-Black Elastomer Sensors.

The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. To address these requirements, we have developed "VitalCore", a wearable continuous vital parameter monitoring device in the form of a T-shirt targeting the uninterrupted monitoring of respiration, pulse, and actigraphy. VitalCore uses polymer-based stretchable resistive bands as the primary sensor to capture breathing and pulse patterns from chest expansion. The carbon black-impregnated polymer is implemented in a U-shaped configuration and attached to the T-shirt with "interfacing" material along with the accompanying electronics. In this paper, VitalCore is bench tested and compared to gold standard respiration and pulse measurements to verify its functionality and further to assess the quality of data captured during sleep and during light exercise (walking). We show that these polymer-based sensors could identify respiratory peaks with a sensitivity of 99.44%, precision of 96.23%, and false-negative rate of 0.557% during sleep. We also show that this T-shirt configuration allows the wearer to sleep in all sleeping positions with a negligible difference of data quality. The device was also able to capture breathing during gait with 88.9%-100% accuracy in respiratory peak detection.

Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall.

This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the 19th century, several techniques have been proposed to detect the mechanical vibrations caused by cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject's chest and provides information on major events of the cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto the subject's chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency (HF) components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency (LF) component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results of this feasibility study suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.

WCTECGdb: A 12-Lead Electrocardiography Dataset Recorded Simultaneously with Raw Exploring Electrodes' Potential Directly Referred to the Right Leg.

With this paper we communicated the existence of a surface electrocardiography (ECG) recordings dataset, named WCTECGdb, that aside from the standard 12-lead signals includes the raw electrode biopotential for each of the nine exploring electrodes refereed directly to the right leg. This dataset, comprises of 540 ten second segments recorded from 92 patients at Campbelltown Hospital, NSW Australia, and is now available for download from the Physionet platform. The data included in the dataset confirm that the Wilson's Central Terminal (WCT) has a relatively large amplitude (up to 247% of lead II) with standard ECG characteristics such as a p-wave and a t-wave, and is highly variable during the cardiac cycle. As further examples of application for our data, we assess: (1) the presence of a conductive pathway between the legs and the heart concluding that in some cases is electrically significant and (2) the initial assumption about the limbs potential stating the dominance of the left arm concluding that this is not always the case and that might requires case to case assessment.

The MIDA Mortality Risk Score: development and external validation of a prognostic model for early and late death in degenerative mitral regurgitation.

Aims: In degenerative mitral regurgitation (DMR), lack of mortality scores predicting death favours misperception of individual patients' risk and inappropriate decision-making. Methods and results: The Mitral Regurgitation International Database (MIDA) registries include 3666 patients (age 66 ± 14 years; 70% males; follow-up 7.8 ± 5.0 years) with pure, isolated, DMR consecutively diagnosed by echocardiography at tertiary (European/North/South-American) centres. The MIDA Score was derived from the MIDA-Flail-Registry (2472 patients with DMR and flail leaflet-Derivation Cohort) by weighting all guideline-provided prognostic markers, and externally validated in the MIDA-BNP-Registry (1194 patients with DMR and flail leaflet/prolapse-Validation Cohort). The MIDA Score ranged from 0 to 12 depending on accumulating risk factors. In predicting total mortality post-diagnosis, the MIDA Score showed excellent concordance both in Derivation Cohort (c = 0.78) and Validation Cohort (c = 0.81). In the whole MIDA population (n = 3666 patients), 1-year mortality with Scores 0, 7-8, and 11-12 was 0.4, 17, and 48% under medical management and 1, 7, and 14% after surgery, respectively (P < 0.001). Five-year survival with Scores 0, 7-8, and 11-12 was 98 ± 1, 57 ± 4, and 21 ± 10% under medical management and 99 ± 1, 82 ± 2, and 57 ± 9% after surgery (P < 0.001). In models including all guideline-provided prognostic markers and the EuroScoreII, the MIDA Score provided incremental prognostic information (P ≤ 0.002). Conclusion: The MIDA Score may represent an innovative tool for DMR management, being able to position a given patient within a continuous spectrum of short- and long-term mortality risk, either under medical or surgical management. This innovative prognostic indicator may provide a specific framework for future clinical trials aiming to compare new technologies for DMR treatment in homogeneous risk categories of patients.

Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method.

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

A Comparison of Reflective Photoplethysmography for Detection of Heart Rate, Blood Oxygen Saturation, and Respiration Rate at Various Anatomical Locations.

Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of this paper is to identify a single location on the human anatomy whereby a single 1 cm × 1 cm non-invasive sensor could simultaneously measure heart rate (HR), blood oxygen saturation (SpO2), and respiration rate (RR), at rest and while walking. To evaluate the best anatomical location, we analytically compared eight anatomical locations for photoplethysmography (PPG) sensors simultaneously acquired by a single microprocessor at rest and while walking, with a comparison to a commercial pulse oximeter and respiration rate ground truth. Our results show that the forehead produced the most accurate results for HR and SpO2 both at rest and walking, however, it had poor RR results. The finger recorded similar results for HR and SpO2, however, it had more accurate RR results. Overall, we found the finger to be the best location for measurement of all three parameters at rest; however, no site was identified as capable of measuring all parameters while walking.

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented.

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

Peripheral vascular disease assessment in the lower limb: a review of current and emerging non-invasive diagnostic methods.

BACKGROUND: Worldwide, at least 200 million people are affected by peripheral vascular diseases (PVDs), including peripheral arterial disease (PAD), chronic venous insufficiency (CVI) and deep vein thrombosis (DVT). The high prevalence and serious consequences of PVDs have led to the development of several diagnostic tools and clinical guidelines to assist timely diagnosis and patient management. Given the increasing number of diagnostic methods available, a comprehensive review of available technologies is timely in order to understand their limitations and direct future development effort. MAIN BODY: This paper reviews the available diagnostic methods for PAD, CVI, and DVT with a focus on non-invasive modalities. Each method is critically evaluated in terms of sensitivity, specificity, accuracy, ease of use, procedure time duration, and training requirements where applicable. CONCLUSION: This review emphasizes the limitations of existing methods, highlighting a latent need for the development of new non-invasive, efficient diagnostic methods. Some newly emerging technologies are identified, in particular wearable sensors, which demonstrate considerable potential to address the need for simple, cost-effective, accurate and timely diagnosis of PVDs.

A Contactless Sensor for Pacemaker Pulse Detection: Design Hints and Performance Assessment.

Continuous monitoring of pacemaker activity can provide valuable information to improve patients' follow-up. Concise information is stored in some types of pacemakers, whereas ECG can provide more detailed information, but requires electrodes and cannot be used for continuous monitoring. This study highlights the possibility of a continuous monitoring of pacemaker pulses by sensing magnetic field variations due to the current pulses. This can be achieved by means of a sensor coil positioned near the patient's thorax without any need for physical contact. A simplified model of coil response to pacemaker pulses is presented in this paper, along with circuits suitable for pulse detection. In vitro tests were carried out using real pacemakers immersed in saline solution; experimental data were used to assess the accuracy of the model and to evaluate the sensor performance. It was found that the coil signal amplitude decreases with increasing distance from the pacemaker lead wire. The sensor was able to easily perform pacemaker spike detection up to a distance of 12 cm from the pacemaker leads. The stimulation rate can be measured in real time with high accuracy. Since any electromagnetic pulse triggers the same coil response, EMI may corrupt sensor measurements and thus should be discriminated.

A Piezoresistive Sensor to Measure Muscle Contraction and Mechanomyography.

Measurement of muscle contraction is mainly achieved through electromyography (EMG) and is an area of interest for many biomedical applications, including prosthesis control and human machine interface. However, EMG has some drawbacks, and there are also alternative methods for measuring muscle activity, such as by monitoring the mechanical variations that occur during contraction. In this study, a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR) which is able to measure muscle contraction is presented. The sensor, applied on the skin through a rigid dome, senses the mechanical force exerted by the underlying contracting muscles. Although FSR creep causes output drift, it was found that appropriate FSR conditioning reduces the drift by fixing the voltage across the FSR and provides voltage output proportional to force. In addition to the larger contraction signal, the sensor was able to detect the mechanomyogram (MMG), i.e., the little vibrations which occur during muscle contraction. The frequency response of the FSR sensor was found to be large enough to correctly measure the MMG. Simultaneous recordings from flexor carpi ulnaris showed a high correlation (Pearson's r > 0.9) between the FSR output and the EMG linear envelope. Preliminary validation tests on healthy subjects showed the ability of the FSR sensor, used instead of the EMG, to proportionally control a hand prosthesis, achieving comparable performances.

On the Einthoven Triangle: A Critical Analysis of the Single Rotating Dipole Hypothesis.

Since its inception, electrocardiography has been based on the simplifying hypothesis that cardinal limb leads form an equilateral triangle of which, at the center/centroid, the electrical equivalent of the cardiac activity rotates during the cardiac cycle. Therefore, it is thought that the three limbs (right arm, left arm, and left leg) which enclose the heart into a circuit, where each branch directly implies current circulation through the heart, can be averaged together to form a stationary reference (central terminal) for precordials/chest-leads. Our hypothesis is that cardinal limbs do not form a triangle for the majority of the duration of the cardiac cycle. As a corollary, the central point may not lie in the plane identified by the limb leads. Using a simple and efficient algorithm, we demonstrate that the portion of the cardiac cycle where the three limb leads form a triangle is, on average less, than 50%.

Low-power transcutaneous current stimulator for wearable applications.

BACKGROUND: Peripheral neuropathic desensitization associated with aging, diabetes, alcoholism and HIV/AIDS, affects tens of millions of people worldwide, and there is little or no treatment available to improve sensory function. Recent studies that apply imperceptible continuous vibration or electrical stimulation have shown promise in improving sensitivity in both diseased and healthy participants. This class of interventions only has an effect during application, necessitating the design of a wearable device for everyday use. We present a circuit that allows for a low-power, low-cost and small form factor implementation of a current stimulator for the continuous application of subthreshold currents. RESULTS: This circuit acts as a voltage-to-current converter and has been tested to drive + 1 to - 1 mA into a 60 k[Formula: see text] load from DC to 1 kHz. Driving a 60 k[Formula: see text] load with a 2 mA peak-to-peak 1 kHz sinusoid, the circuit draws less than 21 mA from a 9 V source. The minimum operating current of the circuit is less than 12 mA. Voltage compliance is ± 60 V with just 1.02 mA drawn by the high voltage current drive circuitry. The circuit was implemented as a compact 46 mm × 21 mm two-layer PCB highlighting its potential for use in a body-worn device. CONCLUSIONS: No design to the best of our knowledge presents comparably low quiescent power with such high voltage compliance. This makes the design uniquely appropriate for low-power transcutaneous current stimulation in wearable applications. Further development of driving and instrumentation circuitry is recommended.