Pareto optimization for electrodes placement: compromises between electrophysiological and practical aspects

Wearable electronics and sensors are increasingly popular for personal health monitoring, including smart shirts containing electrocardiography (ECG) electrodes. Optimal electrode performance requires careful selection of the electrode position. On top of the electrophysiological aspects, practical aspects must be considered due to the dynamic recording environment. We propose a new method to obtain optimal electrode placement by considering multiple dimensions. The electrophysiological aspects were represented by P-, R-, and T-peak of ECG waveform, while the shirt-skin gap, shirt movement, and regional sweat rate represented the practical aspects. This study employed a secondary data set and simulations for the electrophysiological and practical aspects, respectively. Typically, there is no ideal solution that maximizes satisfaction degrees of multiple electrophysiological and practical aspects simultaneously; a compromise is the most appropriate approach. Instead of combining both aspects-which are independent of each other-into a single-objective optimization, we used multi-objective optimization to obtain a Pareto set, which contains predominant solutions. These solutions may facilitate the decision-makers to decide the preferred electrode locations based on application-specific criteria. Our proposed approach may aid manufacturers in making decisions regarding the placement of electrodes within smart shirts.

[1]  Yan Jiang,et al.  Cloth simulation for Chinese traditional costumes , 2018, Multimedia Tools and Applications.

[2]  Jeong Yim Lee,et al.  Comparison of body shape between USA and Korean women , 2007 .

[3]  W. Shimizu,et al.  Validation of wearable textile electrodes for ECG monitoring , 2019, Heart and Vessels.

[4]  Olaf Dössel,et al.  P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference , 2016, Biomedizinische Technik. Biomedical engineering.

[5]  José Carlos Príncipe,et al.  Integrate and Fire Pulse Train Automaton for QRS detection , 2014, IEEE Transactions on Biomedical Engineering.

[6]  Agnes Psikuta,et al.  Quantitative validation of 3D garment simulation software for determination of air gap thickness in lower body garments , 2017 .

[7]  Michael T. M. Emmerich,et al.  A tutorial on multiobjective optimization: fundamentals and evolutionary methods , 2018, Natural Computing.

[8]  T. Oostendorp,et al.  Interpolation on a triangulated 3D surface , 1989 .

[9]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

[10]  Murat Kaya Yapici,et al.  Wearable and Flexible Textile Electrodes for Biopotential Signal Monitoring: A review , 2019, Electronics.

[11]  Slavica Bogović,et al.  Computational Design of Functional Clothing for Disabled People , 2019, TEKSTILEC.

[12]  Andrew Lowe,et al.  Development and validation of Motion Artefact Rejection System (MARS) for electrocardiography using novel skin-stretch estimation approach , 2020 .

[13]  Subhas Chandra Mukhopadhyay,et al.  Wearable Sensors for Human Activity Monitoring: A Review , 2015, IEEE Sensors Journal.

[14]  Hoi-Jun Yoo,et al.  Your Heart on Your Sleeve: Advances in Textile-Based Electronics Are Weaving Computers Right into the Clothes We Wear , 2013, IEEE Solid-State Circuits Magazine.

[15]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[16]  Roman Trobec,et al.  Synthesis of the 12-Lead Electrocardiogram From Differential Leads , 2011, IEEE Transactions on Information Technology in Biomedicine.

[17]  Arzu Vuruskan,et al.  Identification of female body shapes based on numerical evaluations , 2011 .

[18]  Madjid Tavana,et al.  Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS , 2016, Expert Syst. Appl..

[19]  Roman Trobec,et al.  Electrocardiographic Systems With Reduced Numbers of Leads—Synthesis of the 12-Lead ECG , 2014, IEEE Reviews in Biomedical Engineering.

[20]  Mary Jahrsdoerfer,et al.  Clinical usefulness of the EASI 12-lead continuous electrocardiographic monitoring system. , 2005, Critical care nurse.

[21]  J. Webster,et al.  Dry electrodes for electrocardiography , 2013, Physiological measurement.

[22]  Chris D. Nugent,et al.  Optimal Electrocardiographic Lead Systems: Practical Scenarios in Smart Clothing and Wearable Health Systems , 2008, IEEE Transactions on Information Technology in Biomedicine.

[23]  G. Cho,et al.  Polyurethane nanoweb-based textile sensors treated with single-walled carbon nanotubes and silver nanowire , 2018, Textile Research Journal.

[24]  Agnes Psikuta,et al.  A validation methodology and application of 3D garment simulation software to determine the distribution of air layers in garments during walking , 2018 .

[25]  Chris D. Nugent,et al.  Eigenleads: ECG Leads for Maximizing Information Capture and Improving SNR , 2010, IEEE Transactions on Information Technology in Biomedicine.

[26]  Lili Wang,et al.  Reviews of wearable healthcare systems: Materials, devices and system integration , 2020 .

[27]  Nickolas S. Sapidis,et al.  A new methodology for the development of sizing systems for the mass customization of garments , 2010 .

[28]  George Havenith,et al.  Body mapping of sweating patterns in male athletes in mild exercise-induced hyperthermia , 2010, European Journal of Applied Physiology.

[29]  Wei Tang,et al.  A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[30]  Jari Hyttinen,et al.  A motion artifact generation and assessment system for the rapid testing of surface biopotential electrodes. , 2015, Physiological measurement.

[31]  R. Bhattacharya,et al.  Improving conduction and mechanical reliability of woven metal interconnects , 2012, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[32]  A. Holden,et al.  Multichannel electrocardiogram diagnostics for the diagnosis of arrhythmogenic right ventricular dysplasia. , 2018, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[33]  Jari Hyttinen,et al.  Effect of pressure and padding on motion artifact of textile electrodes , 2013, Biomedical engineering online.

[34]  Carmen C. Y. Poon,et al.  Unobtrusive Sensing and Wearable Devices for Health Informatics , 2014, IEEE Transactions on Biomedical Engineering.

[35]  E. Supriyanto,et al.  Improving accuracy of derived 12-lead electrocardiography by waveform segmentation , 2019, Indonesian Journal of Electrical Engineering and Informatics (IJEEI).

[36]  Elizabeth Wissinger Wearable tech, bodies, and gender , 2017 .

[37]  Jean-Yves Tourneret,et al.  P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler , 2010, IEEE Transactions on Biomedical Engineering.

[38]  Dongyi Chen,et al.  Cancellation of motion artifacts in ambulatory ECG signals using TD-LMS adaptive filtering techniques , 2019, J. Vis. Commun. Image Represent..

[39]  Takeshi Kobayashi,et al.  Relationship between Contact Pressure and Motion Artifacts in ECG Measurement with Electrostatic Flocked Electrodes Fabricated on Textile , 2019, Scientific Reports.

[40]  Yuxiang Zhu,et al.  Fit Evaluation during Repetition Interaction in Garment Pattern Design , 2019, Mathematical Problems in Engineering.