Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology

Abstract

The electrophysiological signals recorded by cutaneous electrodes, known as body surface potentials (BSPs), are widely employed biomarkers in medical diagnosis. Despite their widespread application and success in detecting various conditions, the poor spatial resolution of traditional BSP measurements poses a limit to their diagnostic potential. Advancements in the field of bioelectronics have facilitated the creation of compact, high-quality, high-density recording arrays for cutaneous electrophysiology, allowing detailed spatial information acquisition as BSP maps (BSPMs). Currently, the design of electrode arrays for BSP mapping lacks a standardized framework, leading to customizations for each clinical study, limiting comparability, reproducibility, and transferability. This perspective proposes preliminary design guidelines, drawn from existing literature, rooted solely in the physical properties of electrophysiological signals and mathematical principles of signal processing. These guidelines aim to simplify and generalize the optimization process for electrode array design, fostering more effective and applicable clinical research. Moreover, the increased spatial information obtained from BSPMs introduces interpretation challenges. To mitigate this, two strategies are outlined: observational transformations that reconstruct signal sources for intuitive comprehension, and machine learning-driven diagnostics. BSP mapping offers significant advantages in cutaneous electrophysiology with respect to classic electrophysiological recordings and is expected to expand into broader clinical domains in the future.

Keywords: BSPM; electrophysiology; high‐density; mapping; wearables.

Figure 1

a) Diagram of different body surface potentials in the human body and the conditions they help diagnose. b) Amplitude and spectral characteristics of common body surface potentials, c) Time domain representation of several body surface potentials. EEG = electroencephalography, EOG = electrooculography, ERG = electroretinography, ABR = auditory brainstem responses, CAEP = cortical auditory evoked potentials, ECG = electrocardiography, EGEG = electrogastroenterography, EHG = electrohysterography and EMG = electromyography.

Figure 2

a) Diagram of different body surface potential maps across the human body, along with a sample body surface potential observed by a channel in the map. b) Graphical representation of geometrical electrode array design parameters: array outline, electrode layout, electrode shape, electrode area, and inter‐electrode distance.

Figure 3

a) Effect of electrode shape on spatial resolution. Top: transfer function for each electrode shape as a function of spatial frequency, bottom: electrode shape projections along with EMG waveforms captured from each. b) Effect of electrode area on spatial resolution and signal‐to‐noise ratio of conventional ECG data. As the area increases the spatial resolution and signal noise decrease due to averaging. The optimal electrode area is the largest area that does not cause an attenuation of −3 dB across the entire signal bandwidth, c) the effect of inter‐electrode distance on spatial resolution. As the density of electrodes decreases, spatial features are not captured, d) Graphical representation of the proposed strategy for the implementation of non‐uniform electrode distribution arrays. The area under study should be subdivided into regions with constant signal physical properties to determine optimal electrode parameters, e) truncation effect, and external signal interference effects caused by spatial under‐ and over‐sampling. Blue: Over‐sampling of the region of interest causes external interference sources to affect recordings, green: under‐sampling causes reconstruction distortions due to lack of information at the boundary, red: a compromise between both effects is achieved by padding the area of interest with a thin additional layer of electrodes.

Figure 4

The process of BSP mapping in clinical diagnosis. First, a clinical application is targeted. Second, the geometrical parameters of the BSPM arrays are optimized for the given application. Third, the BSPs are recorded and BSPMs are generated through interpolation. Once the data is gathered two strategies for data interpretation can be taken: a diagnostic approach, in which BSPM data is employed directly to train machine learning classifiers able to diagnose conditions, and an observational approach, in which BSPM data is transformed to simplify human diagnosis.