Stringlike pulse quantification study by pulse wave in 3D pulse mapping

Artificial Intelligence-Enabled Novel Atrial Fibrillation Diagnosis System Using 3D Pulse Perception Flexible Pressure Sensor Array

Atrial fibrillation (AF) as one of the most common cardiovascular diseases has attracted great attention due to its high disability and mortality rate. Thus, a timely and effective recognition method for AF is of great importance for diagnosing and preventing it. Herein, we proposed a novel intelligent sensing and recognition system for AF which combined Traditional Chinese Medicine (TCM), flexible wearable electronic devices, and artificial intelligence. Experiment and simulation synergistically verified that the flexible pressure sensor arrays designed according to the TCM theory could synchronously obtain the 3D pulses at Cun, Guan, and Chi. Combined with a homemade signal acquisition system and the pulse signals labeled by doctors of cardiovascular diseases, the differences in the 3D pulse signals between ones with AF and without can be picked up clearly. Enabled the convolutional neural network (CNN) and the pulse database, the recognition model was formed with a recognition rate of up to 90%. As a proof of concept, the artificial intelligence-enabled novel atrial fibrillation diagnosis system has been used to detect patients with AF in hospitals, showing 80% recognition rate. This work provides a new strategy to precisely diagnose and remotely treat AF, as well as to accelerate the development of Modern Chinese Medicine treatment.

Highly Sensitive Flexible Capacitive Pressure Sensor Based on a Multicross-Linked Dual-Network Ionic Hydrogel for Blood Pressure Monitoring Applications

Flexible capacitive pressure sensors based on ionic hydrogels (IHs) have garnered significant attention in the field of wearable technology. However, the vulnerability of traditional single-network hydrogels to mechanical damage and the complexity associated with preparing double-network hydrogels present challenges in developing a highly sensitive, easily prepared, and durable IH-based flexible capacitive pressure sensor. This study introduces a novel multicross-linked dual-network IH achieved through the physical and chemical cross-linking of polymers polyvinyl alcohol (PVA) and chitosan (CS), ionic solution H3PO4, and cross-linking agent gum arabic. Flexible capacitive pressure sensors, characterized by high sensitivity and a broad pressure range, are fabricated by employing mesh as templates to design cut-corner cube microstructures with high uniformity and controllability on the IHs. The sensor exhibits high sensitivity across a wide pressure range (0-290 kPa) and with excellent features such as high resolution (∼1.3 Pa), fast response-recovery time (∼11 ms), and repeatable compression stability at 25 kPa (>2000 cycles). The IHs as a dielectric layer demonstrate long-term water retention properties, enabling exposure to air for up to 100 days. Additionally, the developed sensor shows the ability to accurately measure the pulse wave within the small pressure range. By combining the pulse wave acquired by the sensor with a trained neural network model, we achieve successful blood pressure (BP) prediction, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. Ultimately, the sensor proposed in this study holds promising prospects for broad applications in high-precision wearable medical electronic devices.

A comprehensive review on technological advancements for sensor-based Nadi Pariksha: An ancient Indian science for human health diagnosis

Nadi Pariksha is a significant, rather symbolic term for Ayurveda. Ancient Ayurvedic literature has prominently stated its importance in the judgment of Tridoshas (Vata, Pitta, and Kapha) which are the base of ailment diagnosis and prediction. The knowledge about Nadi Pariksha is uncovered in various ancient Ayurvedic literature like Ravansamhita, Bhavprakash, Nadivigyan by Kanad, Sharangdhar, and Yogratnakar. The various Nadi parameters are indicative of the diagnosis of diseases. These techniques were used as popular diagnostic tools in Indian culture from ancient days. Still, nowadays, these are not being used explicitly due to the lack of expertise, so it is necessary to establish their results once gained so that they can be used along with technical aspects in today's era. Ayurveda believes that all the elements of the Universe are present in any human body in minute, proportionate quantity, and the Nadi represents these elements, that is, Vata, Pitta, and Kapha (VPK). To facilitate the Nadi Pariksha using appropriate sensors may help the Ayurveda practitioners diagnose Prakriti and predict some diseases, making the Nadi Pariksha more reliable and faster. This review paper lists, 2 books and 67 research papers, mostly from countries like India, China, Japan, Korea, etc., from various reputed databases. The review primarily concentrates on six research themes: sensors and devices used for Nadi signal acquisition, signal pre-processing methods, feature extraction methods, feature selection approaches, classification practices, diseases diagnosed, and results attained. The paper also reviews the challenges in implementing the automated Nadi Pariksha with technological aid, which is a necessity of this period and is a very vibrant research arena. Yet significant work remains to be done, like bridging the gaps between technical and commercial development, and the procedure standardization is also required.

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

Introduction: In studies of pulse wave analysis, single-channel sensors only adopt single temporal pulse signals without spatial information to show pulse-feeling patterns. Multi-channel arterial pulse signals, also named as three-dimensional pulse images (3DPIs), provide the spatial and temporal characteristics of radial pulse signals. When involving single or few-channel sensors, pressing offsets have substantial impacts on obtaining inaccurate physiological parameters like tidal peak (P2). Methods: This study discovers the pressing offsets in multi-channel pulse signals and analyzes the relationship between the pressing offsets and time of P2 (T2) by qualifying the pressing offsets. First, we employ a data acquisition system to capture 3DPIs. Subsequently, the errorT2 is developed to qualify the pressing offsets. Results: The outcomes display a central low and peripheral high pattern. Additionally, the errorT2 increase as the distances from the artery increase, particularly at the radial ends of the blood flow direction. For every 1 mm increase in distances between sensing elements and center sensing elements, the errorT2 in the radial direction escalates by 4.87%. When the distance is greater than 3.42 mm, the errorT2 experiences a sudden increase. Discussion: The results show that increasing the sensor channels can overcome the pressing offsets in radial pulse signal acquisition.

Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System

Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

In-Memory Tactile Sensor with Tunable Steep-Slope Region for Low-Artifact and Real-Time Perception of Mechanical Signals

A tactile sensor needs to perceive static pressures and dynamic forces in real-time with high accuracy for early diagnosis of diseases and development of intelligent medical prosthetics. However, biomechanical and external mechanical signals are always aliased (including variable physiological and pathological events and motion artifacts), bringing great challenges to precise identification of the signals of interest (SOI). Although the existing signal segmentation methods can extract SOI and remove artifacts by blind source separation and/or additional filters, they may restrict the recognizable patterns of the device, and even cause signal distortion. Herein, an in-memory tactile sensor (IMT) with a dynamically adjustable steep-slope region (SSR) and nanocavity-induced nonvolatility (retention time >1000 s) is proposed on the basis of a machano-gated transistor, which directly transduces the tactile stimuli to various dope states of the channel. The programmable SSR endows the sensor with a critical window of responsiveness, realizing the perception of signals on demand. Owing to the nonvolatility of the sensor, the mapping of mechanical cues with high spatiotemporal accuracy and associative learning between two physical inputs are realized, contributing to the accurate assessment of the tissue health status and ultralow-power (about 25.1 μW) identification of an occasionally occurring tremor.

Wearable multichannel pulse condition monitoring system based on flexible pressure sensor arrays

Pulse diagnosis is an irreplaceable part of traditional Chinese medical science. However, application of the traditional pulse monitoring method was restricted in the modernization of Chinese medical science since it was difficult to capture real signals and integrate obscure feelings with a modern data platform. Herein, a novel multichannel pulse monitoring platform based on traditional Chinese medical science pulse theory and wearable electronics was proposed. The pulse sensing platform simultaneously detected pulse conditions at three pulse positions (Chi, Cun, and Guan). These signals were fitted to smooth surfaces to enable 3-dimensional pulse mapping, which vividly revealed the shape of the pulse length and width and compensated for the shortcomings of traditional single-point pulse sensors. Moreover, the pulse sensing system could measure the pulse signals from different individuals with different conditions and distinguish the differences in pulse signals. In addition, this system could provide full information on the temporal and spatial dimensions of a person's pulse waveform, which is similar to the true feelings of doctors' fingertips. This innovative, cost-effective, easily designed pulse monitoring platform based on flexible pressure sensor arrays may provide novel applications in modernization of Chinese medical science or intelligent health care.

Fourier Series Analysis for Novel Spatiotemporal Pulse Waves: Normal, Taut, and Slippery Pulse Images

In this article, a three-dimensional pulse image (3DPI) instead of a one-dimensional temporal pulse wave is studied to elucidate its spatiotemporal characteristics. To check the spatial and temporal properties of 3DPI, adopted is Fourier series, in which a ratio (r) is defined as one amplitude divided by the sum of the first three amplitudes of harmonics. A ratio sequence is constituted from 70 to 90 ratios in a heartbeat with 70–90 3DPIs by sampling. Twenty-four subjects (14 males and 10 females with age of 22.2 ± 3.7 years, 20.4 ± 1.4 BMI, and 112.1 ± 4.7 mmHg systolic blood pressure) are involved in this research. There are significant statistical differences in the groups of the normal, taut, and slippery 3DPIs by the first harmonic ratio average (r1¯) and ratio difference (Δr₁) produced from the ratio sequence. The proposed method of this study gives us a novel viewpoint to clarify the spatiotemporal characteristics of pulse images, which can translate and quantize the pulse feeling in Chinese medicine texts.

Analysis of Pulse Signals Based on Array Pulse Volume

OBJECTIVE

To collect and analyze multi-dimensional pulse diagram features with the array sensor of a pressure profile system (PPS) and study the characteristic parameters of the new multi-dimensional pulse diagram by pulse diagram analysis technology.

METHODS

The pulse signals at the Guan position of left wrist were acquired from 105 volunteers at the Shanghai University of Traditional Chinese Medicine. We obtained the pulse data using an array sensor with 3×4 channels. Three dimensional pulse diagrams were constructed for the validated pulse data, and the array pulse volume (APV) parameter was computed by a linear interpolation algorithm. The APV differences among normal pulse (NP), wiry pulse (WP) and slippery pulse (SP) were analyzed using one-way analysis of variance. The coefficients of variation (CV) were calculated for WP, SP and NP.

RESULTS

The APV difference between WP and NP in the 105 volunteers was statistically significant (6.26±0.28 vs. 6.04±0.36, P=0.048), as well as the difference between WP and SP (6.26±0.28 vs. 6.07±0.46, P=0.049). However, no statistically significant difference was found between NP and SP (P=0.75). WP showed a similar CV (4.47%) to those of NP (5.96%) and SP (7.58%).

CONCLUSION

The new parameter APV could differentiate between NP or SP and WP. Accordingly, APV could be considered an useful parameter for the analysis of array pulse diagrams in Chinese medicine.

Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features.

Neural network study for standardizing pulse-taking depth by the width of artery

To carry out a pulse diagnosis, a traditional Chinese medicine (TCM) physician presses the patient's wrist artery at three incremental depths, namely Fu (superficial), Zhong (medium), and Chen (deep). However, the definitions of the three depths are insufficiently clear for use with modern pulse diagnosis instruments (PDIs). In this paper, a quantitative method is proposed to express the pulse-taking depths based on the width of the artery (WA). Furthermore, an index, α, is developed for estimating WA for PDI application. The α value is obtained using an artificial neural network (ANN) model with contact pressure (CP) and sensor displacement (SD) as the inputs. The WA and SD data from an ultrasound instrument and CP and SD data from a PDI were analyzed. The results show that the mean prediction error and the standard deviation (STD) of the ANN model was 1.19% and 0.0467, respectively. Comparing the ANN model with the SD model by statistical method, it showed significant difference and the improvement in the mean prediction error and the STD was 71.62% and 29.78%, respectively. The α value can thus map WA with less individual variation than that of the values estimated directly using the SD model. Pulse signals at different depths thus can be acquired according to α value while using a PDI, providing TCM physicians with more reliable pulse information.

Traditional East Asian medicine: how to understand and approach diagnostic findings and patterns in a modern scientific framework?

Research into the diagnostic methods and patterns of traditional East Asian medical (TEAM) systems of practice such as acupuncture and herbal medicine face certain challenges due to the nature of thinking in TEAM and the subjective basis of judgments made in practice. The TEAM-based diagnosis can take into account various findings and signs such as the appearance of the tongue, palpable qualities of the radial pulses, palpable qualities and findings on the abdomen, the complexion of the patient and so on. Both diagnostic findings and the patterns of diagnosis cannot be assumed to have objective bases or to be causally related to the complaints of the patient. However, the diagnoses of TEAM based acupuncture and herbal medicine have tended to look at pictures of the whole patient and rather than focus on a particular symptom, they have looked across a myriad of signs and symptoms to decide or identify the 'pattern' of diagnosis according to the theory in question. Although open for selective and subjective biases each diagnosis pattern always comes with a prescribed treatment tailored to the pattern. Further, the same research requirements needed for the validation of the diagnoses are needed also for these clinical observations and judgments. Hence, it is necessary, albeit challenging for research on TEAM diagnoses to first address these issues before proceeding to more complex investigations such as the development of instruments for making diagnostic observations, instruments for forming diagnostic conclusions or studies investigating the physiological bases of the diagnostic patterns. Preliminary work has started and instruments have been made, but we suggest that any instrumentation must necessarily be first validated by matching of the calibrated or scaled observations or judgments to observations made and agreed upon by relevant experts. Reliability of all observations and judgments are needed before any other tool, technology or more advanced approach can proceed and also whenever the natural system of diagnosis-treatment is applied in clinical trials. In this paper the authors highlight the core problems and describe a step wise process for addressing them.