Wearable Electrocardiography Research Articles

Probes of New Types of Electrodes of ECG

Long-term electrocardiography (ECG) monitoring can scrutinize human health, which is very essential for the early detection and treatment of cardiovascular disorders. For present, commercially used silver/silver chloride (Ag/AgCl) electrodes have drawbacks, and these would be more obvious in long-term monitoring. Therefore, people are developing new types of electrodes for ECG from different perspectives. At present, innovations are made: 1) Applying conductive thread into sewing to make textile electrodes for wearable ECG monitoring. 2) New material (Graphite Based Electrode) replacing the conventionally used Ag/AgCl.

(Digital Presentation) A Low-Modulus, Soft and Stretchable Wearable Electrocardiography Sensor-System Patch

Electrocardiography (ECG) is a primary tool for diagnosis of various types of disease and disorders such as arrhythmias, coronary heart disease, heart attack, mental disorders, etc. Reliable wearable ECG devices can be a valuable tool in early diagnosis and effective treatment of such disease and disorders. Convention wearable ECG sensor-systems are rigid, bulky, expensive, and the recorded ECG signals using these wearable devices lack required accuracy for medical diagnosis mainly due to motion artifacts that is originated from slippage of sensors on skin and/or instable interface between the soft sensor and the rigid electronic circuits.Here, we report a low-modulus, soft and stretchable wearable electrocardiography (ECG) sensor-system patch which enables continuous and long-term recording of ECG signals with minimal motion artifacts. This system consists of two reusable, detachable-attachable patches: sensor patch and circuit patch, as shown in Figure 1a. The reusable multi-wall carbon nanotube (MWCNT) based sensor patch is fabricated on stretchable elastomer substrate by our developed cost and time-effective method. Reusable, front-end and wireless circuit patch is made by integration of the off-the-shelf integrated circuit components with three-dimensional (3D) stretchable-deformable interconnects (SDI). SDIs are 3D springs of liquid metal (EGaIn). Unlike other reported liquid metal based interconnects, SDI has been designed in 3D form to increase the stretchability and make it low-modulus and robust to deformations in all directions. The stretchability of SDI structure is beyond 500% (the limit of our measurement system) without any negligible change in electrical resistivity of the interconnects. With less than 1% change in the resistance of SDIs after 50,000 cycles of applied tensile strain of 200%, the SDI structure ensures reliable stretchability and durability of the circuit patch for long-term electrocardiography.The sensor patch is soft, stretchable and it is fabricated using a low-cost scalable method. This soft and stretchable ECG sensor-system shows comparable performance with respect to medical grade Ag/AgCl wet gel electrodes. Our CNT-PDMS sensors shows low electrode-skin interface impedance and low susceptibility to motion artifact which similar of it in medical grade Ag/AgCl wet gel sensors (Fig. 1b). Further, it is waterproof, reusable and comfortable to wear.The circuit patch includes a rechargeable battery and ECG signals is wirelessly sent to a personal device such as cellphone, tablet and laptop through Bluetooth low energy (BLE) and it is displayed in real-time (Fig. 1c) Our developed sensor-system is a multi-use platform which can be applied for the detection of EMG, EEG or EOG signals. Figure 1

A Wearable Electrocardiography Armband Resilient Against Artifacts

Electrocardiography (ECG) is an essential technique to assess cardiovascular conditions and monitor physical activities. While the concept is mature, issues surrounding sampling convenience and device adoption as well as maintaining signal quality under artifacts remain a problem. In this article, we present a high-performing wearable ECG armband on the upper left arm. It is equipped with miniaturized hardware, capable of data storage and wireless communication. We evaluate different electrode configurations by conducting ECG measurements both at the static state and under motion and using improved algorithms to quantify data quality and assess the agreement between the proposed new technique and the gold standard. The optimal electrode position is determined by balancing wearable suitability and signal quality. We propose an electronic textile (E-textile) armband with improved design. It offers favorable wearing comfort and a fashionable appearance without sacrificing data quality. Its contact pressure is measured to get a better picture of intimacy and clothing comfort. Our system provides real-time and noise-resilient ECG data without interrupting daily life and can be implemented in use cases that warrant continuous ECG monitoring.

Intervention in the Timeliness of Two Electrocardiography Types for Patients in the Emergency Department With Chest Pain: Randomized Controlled Trial.

BackgroundIn the emergency department (ED), the result obtained using the 12-lead electrocardiography (ECG) is the basis for diagnosing and treating patients with chest pain. It was found that performing ECG at the appropriate time could improve treatment outcomes. Hence, a wearable ECG device with a timer can ensure that the findings are continuously recorded.ObjectiveWe aimed to compare the time accuracy of a single-patch 12-lead ECG (SP-ECG) with that of conventional ECG (C-ECG). We hypothesized that SP-ECG would result in better time accuracy.MethodsAdult patients who visited the emergency room with chest pain but were not in shock were randomly assigned to one of the following 2 groups: the SP-ECG group or the C-ECG group. The final analysis included 33 (92%) of the 36 patients recruited. The primary outcome was the comparison of the time taken by the 2 groups to record the ECG. The average ages of the participants in the SP-ECG and C-ECG groups were 63.7 (SD 18.4) and 58.1 (SD 12.4) years, respectively.ResultsWith a power of 0.95 and effect sizes of 0.05 and 1.36, the minimum number of samples was calculated. The minimum sample size for each SP-ECG and C-ECG group is 15.36 participants, assuming a 20% dropout rate. As a result, 36 patients with chest pain participated, and 33 of them were analyzed. The timeliness of SP-ECG and C-ECG for the first follow-up ECG was 87.5% and 47.0%, respectively (P=.74). It was 75.0% and 35.2% at the second follow-up, respectively (P=.71).ConclusionsContinuous ECG monitoring with minimal interference from other examinations is feasible and essential in complex ED situations. However, the precision of SP-ECG has not yet been proved. Nevertheless, the application of SP-ECG is expected to improve overcrowding and human resource shortages in EDs, though more research is needed.Trial RegistrationClinicalTrials.gov NCT04114760; https://clinicaltrials.gov/ct2/show/NCT04114760

Ear Electrocardiography With Soft Graphene Textiles for Hearable Applications

Ear-centered physiological recordings offer rich amount of information originated by neural and cardiac activities. This letter investigates possible applications of graphene-based textiles for behind-the-ear discreet and unobtrusive electrocardiography (ECG) monitoring. We systematically analyzed the most suitable electrode positioning for wearable ear ECG and identified the mastoid area behind the ear helix to be suitable for cardiac monitoring, where the collected signals had a mean signal-to-noise ratio of 29.87 dB. Later, graphene textiles were positioned on the selected locations, and we assessed the feasibility of a wearable headphone design for ear ECG through synchronous recording alongside conventional lead-I placement. We anticipate that contributions here are directly applicable to the extraction of heartrate, heart rate variability, and other cardiac P-QRS-T parameters for remote health monitoring, as well as applications requiring vital signs for real-time stress or anxiety classification, such as virtual reality headsets.

Cost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices

Undiagnosed atrial fibrillation (AF) is an important cause of stroke. Screening for AF using wrist-worn wearable devices may prevent strokes, but their cost-effectiveness is unknown. To evaluate the cost-effectiveness of contemporary AF screening strategies, particularly wrist-worn wearable devices. This economic evaluation used a microsimulation decision-analytic model and was conducted from September 8, 2020, to May 23, 2022, comprising 30 million simulated individuals with an age, sex, and comorbidity profile matching the US population aged 65 years or older. Eight AF screening strategies, with 6 using wrist-worn wearable devices (watch or band photoplethysmography, with or without watch or band electrocardiography) and 2 using traditional modalities (ie, pulse palpation and 12-lead electrocardiogram) vs no screening. The primary outcome was the incremental cost-effectiveness ratio, defined as US dollars per quality-adjusted life-year (QALY). Secondary measures included rates of stroke and major bleeding. In the base case analysis of this model, the mean (SD) age was 72.5 (7.5) years, and 50% of the individuals were women. All 6 screening strategies using wrist-worn wearable devices were estimated to be more effective than no screening (range of QALYs gained vs no screening, 226-957 per 100 000 individuals) and were associated with greater relative benefit than screening using traditional modalities (range of QALYs gained vs no screening, -116 to 93 per 100 000 individuals). Compared with no screening, screening using wrist-worn wearable devices was associated with a reduction in stroke incidence by 20 to 23 per 100 000 person-years but an increase in major bleeding by 20 to 44 per 100 000 person-years. The overall preferred strategy was wearable photoplethysmography, followed conditionally by wearable electrocardiography with patch monitor confirmation, which had an incremental cost-effectiveness ratio of $57 894 per QALY, meeting the acceptability threshold of $100 000 per QALY. The cost-effectiveness of screening was consistent across multiple scenarios, including strata of sex, screening at earlier ages (eg, ≥50 years), and with variation in the association of anticoagulation with risk of stroke in the setting of screening-detected AF. This economic evaluation of AF screening using a microsimulation decision-analytic model suggests that screening using wearable devices is cost-effective compared with either no screening or AF screening using traditional methods.

Derivation of chest‐lead ECG from limb‐lead using temporal convolutional network in variational mode decomposition domain

Standard 12-lead electrocardiography (ECG) is the primary tool for detection and diagnosis of cardiovascular diseases (CVDs). Most wearable ECG devices only provide single limb-lead measurement, limiting their practical use in CVD diagnosis. This study proposes a method of chest-lead ECG reconstruction from a single limb lead using a temporal convolutional network (TCN). The TCN is learned in the variational mode decomposition domain to reduce the non-stationary characteristics of ECG data. Experiments on two public databases suggested that automated diagnosis of CVDs in wearable ECG devices is likely to achieve through the proposed approach.

A 746 nW ECG Processor ASIC Based on Ternary Neural Network.

This paper presents an ultra-low power electrocardiography (ECG) processor application-specific integrated circuit (ASIC) for the real-time detection of abnormal cardiac rhythms (ACRs). The proposed ECG processor can support wearable or implantable ECG devices for long-term health monitoring. It adopts a derivative-based patient adaptive threshold approach to detect the R peaks in the PQRST complex of ECG signals. Two tiny machine learning classifiers are used for the accurate classification of ACRs. A 3-layer feed-forward ternary neural network (TNN) is designed, which classifies the QRS complex's shape, followed by the adaptive decision logics (DL). The proposed processor requires only 1 KB on-chip memory to store the parameters and ECG data required by the classifiers. The ECG processor has been implemented based on fully-customized near-threshold logic cells using thick-gate transistors in 65-nm CMOS technology. The ASIC core occupies a die area of 1.08 mm2. The measured total power consumption is 746 nW, with 0.8 V power supply at 2.5 kHz real-time operating clock. It can detect 13 abnormal cardiac rhythms with a sensitivity and specificity of 99.10% and 99.5%. The number of detectable ACR types far exceeds the other low power designs in the literature.

A Comparative Study of Silver Microflakes in Digitally Printable Liquid Metal Embedded Elastomer Inks for Stretchable Electronics

AbstractPrinted soft conductive materials for stretchable electronics should have low electrical resistivity, high strain limit, and stable electrical properties when stretched. Previously, it has been shown that a bi‐phasic ink composed of silver (Ag) microflakes, eutectic gallium−indium (EGaIn) alloy, and styrene isoprene (SIS) block copolymer is a promising formulation for printed soft electronics and has the potential to satisfy the necessary criteria. In this study, further improvements to the ink formulation are explored, with a focus on how the choice of Ag microflakes affects the electrical and electromechanical properties of the composite. By using specific Ag microflakes, AgInGa‐SIS inks that have conductivity as high as 6.38 × 105 S m−1 and a strain limit of over 1000%, with low electromechanical coupling can be synthesized. More broadly, when comparing the composite with different silver flakes, there is a 176% relative difference in conductivity, >600% difference in strain limit, and 277% relative difference in electromechanical coupling. To demonstrate the applicability of these inks for various use cases such as wearable bioelectronics, interconnects are printed for connecting electronic breakout boards with microcontrollers that provide a stable electrical connection when stretched, and the interconnects and electrodes of a wearable electrocardiography system that monitors the heart pulses in real‐time.

Lightweight Convolutional Neural Network for Real-Time Arrhythmia Classification on Low-Power Wearable Electrocardiograph.

In this study, a lightweight CNN-based electrocardiogram (ECG) classification model is implemented to operate it on a wearable device for real-time arrhythmia detection by efficiently reducing the number of parameters of the model. Ten second-windowed ECGs from three different public ECG databases were used to learn and classify them into four classes: normal sinus rhythm, atrial fibrillation, atrial premature contraction, and ventricular premature contraction. The model implemented in the workstation environment was converted using the TensorFlow Lite framework and then imported into an ARM Cortex-M4 architecture-based nRF52840 microprocessor. The proposed model shows high performance (97.7% accuracy and 97.4% F1 score) with reasonable execution time: 298ms and current consumption: 3.55mA at optimized for speed and execution time: 480ms and current consumption: 3.82mA at optimized for size, respectively.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

Photo-degradable, tough and highly stretchable hydrogels.

We present for the first time highly stretchable and tough hydrogels with controlled light-triggered photodegradation. A double-network of alginate/polyacrylamide (PAAm) is formed by using covalently and ionically crosslinked subnetworks. The ionic Ca2+ alginate interpenetrates a PAAm network covalently crosslinked by a bifunctional acrylic crosslinker containing the photodegradable o-nitrobenzyl (ONB) core instead of the commonly used methylene bisacrylamide (MBAA). Remarkably, due to the developed protocol, the change of the crosslinker did not affect the hydrogel's mechanical properties. The incorporation of photosensitive components in hydrogels allows external temporal control of their properties and tuneable degradation. Cell viability and cell proliferation assays revealed that hydrogels and their photodegradation products are not cytotoxic to the NIH3T3 cell line. In one example of application, we used these hydrogels for bio-potential acquisition in wearable electrocardiography. Surprisingly, these hydrogels showed a lower skin-electrode impedance, compared to the common medical grade Ag/AgCl electrodes. This work lays the foundation for the next generation of tough and highly stretchable hydrogels that are environmentally friendly and can find applications in a variety of fields such as health, electronics, and energy, as they combine excellent mechanical properties with controlled degradation.

Compressed-Domain ECG-based Biometric User Identification Using Task-Driven Dictionary Learning

In recent years, user identification has become crucial for authorized machine access. Electrocardiography (ECG) is a new and rising biometrics signature. Rather than traditional biological traits, ECG cannot be easily imitated. In the long-term monitoring system, the wireless wearable ECG biomedical sensor nodes are resource-limited. Recently, compressive sensing (CS) technology is extensively applied to reduce the power of data transmission and acquisition. The prior CS-based reconstruction process aims at improving energy efficiency with different schemes, and they focus on the performance of reconstruction only. Therefore, we present a sparse coding-based classifier, trained by task-driven dictionary learning (TDDL), to realize low-complexity user identification in compressed-domain directly. TDDL is one of the dictionary learning and designed for classification tasks. It co-optimizes the dictionary and classifier weighting simultaneously, which gives better accuracy. In this article, we are proposing a TDDL-based compression learning algorithm for ECG biometric user identification as this directly identifies user identity (ID) without undergoing reconstruction process and conventional classifier. It can extract necessary information from the compressed-ECG signal directly to save the system power and computational complexity. The algorithm has 2%–10% accuracy improvements compared with state-of-the-art algorithms and maintains low complexity at the same time. Our proposed TDDL-CL will be the better choice in the long-term wearable ECG biometric devices.

A Band-Pass Instrumentation Amplifier Based on a Differential Voltage Current Conveyor for Biomedical Signal Recording Applications

Recently, due to their abundant benefits, current-mode instrumentation amplifiers have received considerable attention in medical instrumentation and read-out circuit for biosensors. This paper is focused on the design of current-mode instrumentation amplifiers for portable, implantable, and wearable electrocardiography and electroencephalography applications. To this end, a CMOS differential voltage second-generation current conveyor (DVCCII) based on a linear transconductor is presented. A new band-pass instrumentation amplifier, based on the designed DVCCII, is also implemented in this paper. The concept of the proposed differential voltage current conveyor and instrumentation amplifier is validated numerically and their predicted performance is presented. The simulation results of the presented circuits were tested for 0.18 μm TSMC CMOS technology in a post layout simulation level using the Cadence Virtuoso tool with a ±0.9 V power supply, and demonstrated that the designed DVCCII has a wide dynamic range of ±400 mV and ±0.85 mA and a power consumption of 148 μW. The layout of the DVCCII circuit occupies a total area of 0.378 μm2. It is shown that the designed DVCCII benefits from good linearity over a wide range of input signals and provides a low input impedance at terminal X. Two versions of the proposed band-pass instrumentation amplifier using pseudo resistances were designed with different specifications for two different applications, namely for EEG and ECG signals. Numerical analyses of both designs show proper outputs and frequency responses by eliminating the undesired artifact and DC component of the EEG and ECG input signals.

Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study

ObjectiveTo explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection. MethodsThirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies. ResultsThree patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research. ConclusionsMulti-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG. SignificanceThis study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy.

SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis

Electrocardiography (ECG) is a sophisticated tool for the diagnosis of myocardial infarction (MI). Deep learning approaches can support MI diagnosis based on ECG data. However, due to privacy issues and the sensitive nature of ECG data, the use of deep learning methods to access large amounts of ECG data for training remains challenging. Insufficient data will lead to a decrease in diagnosis performance. In addition, most deep learning approaches are trained with multiple-lead ECG data, thereby limiting the extension to portable single-lead ECG monitoring devices. Investigating automated single-lead ECG interpretation is a continuing concern in the development of mobile and wearable ECG monitoring devices. This paper proposes an automated myocardial infarction detection model — SLC-GAN — that synthesizes single-lead ECG data with high morphological similarity through generative adversarial networks (GANs). It automatically detects MI using convolutional neural networks (CNNs) with real ECG data and synthetic ECGs from the GAN. The empirical results indicate that our new SLC-GAN method performs impressively compared with other MI classification methodologies on single-lead ECG from the PTB Diagnostic ECG Database. The MI classification accuracy of SLC-GAN reaches 99.06% with 5-fold cross-validation.

Solar powered a wearable Electrocardiography (ECG) device with battery storage

In this article, we propose a wearable ECG device that can be continuously powered by both the sun and artificial lighting via battery charging. The ECG signal acquired by the AD8232 wearable sensor associated with Arduino Nano is sent to the smartphone via Bluetooth. To study the feasibility of solar powered wearable gadget, we have conducted several experiments to analyze the charging speed in both outdoor and indoor conditions. For fully charging 1200 mAh battery, the two solar panels require periods of 5 - 11 hours and 1698 - 1942 hours for outdoor and indoor conditions respectively. The study has shown that the indoor charging is far ineffective as compared to outdoor charging due to spectral mismatch. The current consumption of the device is low and the fully charged battery can act as a buffer to sustain the device for 48 ∼ 60 hours without the need of any further charging.

Highly Stretchable and Skin Adhesive Soft Bioelectronic Patch for Long‐Term Ambulatory Electrocardiography Monitoring

AbstractWearable devices offer a revolutionary approach to ambulatory electrocardiography (ECG) for cardiovascular disease diagnosis. Herein, an adhesive, highly stretchable and conformal (ASC) patch for long‐term ambulatory ECG monitoring is presented. The ASC patch is designed with a “three‐bridge” structure to provide inhomogeneous strain distribution during stretching. Meanwhile, the electrical stability is achieved by stretchable liquid metal interconnects at the domain experiencing larger strain. Moreover, the long‐term usability can be attained by an adhesive layer made of polydopamine/polyacrylamide glycerol–water hydrogel, maintaining good adhesiveness and stretchability for more than two weeks. Altogether, the patch can successfully measure stable high‐quality ECG signals in relaxed, stretched, or underwater conditions. Enhanced mechanical and electrical properties exhibited by our ASC patch confer superior skin‐device interface on either dry or wet conditions than that of current electrodes. The hydrogel‐based patch displays conformal deformation along with the stretched skin. This work provides a scalable prototype for multilead wearable ECG devices and promises new opportunities for medical applications in soft electronics.

Wearable Smart Textiles for Long-Term Electrocardiography Monitoring-A Review.

The continuous and long-term measurement and monitoring of physiological signals such as electrocardiography (ECG) are very important for the early detection and treatment of heart disorders at an early stage prior to a serious condition occurring. The increasing demand for the continuous monitoring of the ECG signal needs the rapid development of wearable electronic technology. During wearable ECG monitoring, the electrodes are the main components that affect the signal quality and comfort of the user. This review assesses the application of textile electrodes for ECG monitoring from the fundamentals to the latest developments and prospects for their future fate. The fabrication techniques of textile electrodes and their performance in terms of skin–electrode contact impedance, motion artifacts and signal quality are also reviewed and discussed. Textile electrodes can be fabricated by integrating thin metal fiber during the manufacturing stage of textile products or by coating textiles with conductive materials like metal inks, carbon materials, or conductive polymers. The review also discusses how textile electrodes for ECG function via direct skin contact or via a non-contact capacitive coupling. Finally, the current intensive and promising research towards finding textile-based ECG electrodes with better comfort and signal quality in the fields of textile, material, medical and electrical engineering are presented as a perspective.

A Wearable Electrocardiography Sensor-System with Three-Dimensional Stretchable Interconnects

The recent COVID-19 pandemic has proven that low-cost wireless wearable medical device which offer reliable continuous health monitoring can be extremely valuable and game changing tools in early diagnosis, control and management of contiguous disease. Current commercial wearables are not reliable and accurate enough for medical diagnosis, further they are rigid, bulky, and expensive.Here we present a skin-mounted, low-cost, soft and stretchable wearable electrocardiography (ECG) sensor-system which enables continuous and wireless ECG recording. The ECG sensor-system consists of low-cost, disposable, soft and stretchable, carbon-based electrodes patch; and a reusable, front-end and wireless circuits patch. The sensor patches are made by our developed low-cost scalable manufacturing method of spray-printing, on breathable, conformal, stretchable medical adhesives. The softness and thinness of electrodes patch ensure the conformability of electrodes to skin, maximizes the quality of sensing by decreasing the electrode-skin impedance and consequently improving the signal to noise ratio (SNR) and furthermore offer comfort to users. The reusable ECG circuit patch consists of three-dimensional stretchable/deformable interconnects (SDI) integrated with off-the-shelf integrated circuit components. SDI is made of microfluidic channels filled with liquid metal (LM). Unless other reported liquid metal based interconnects, SDI has been designed in 3D form to increase the stretchability and make it robust to deformations in all three x, y and z axis. The sensor-system is also waterproof and the developed disposable sensors are breathable and more comfortable than commercial wet gel electrodes. The recorded ECG signal can be sent to a personal device such as tablet and laptop wirelessly and be displayed in real-time. The developed sensor-system is a platform that can be applied for the construction of various types of sensor-systems with other sensing capabilities. Figure 1