Stringlike Pulse Quantification Study by Pulse Wave in 3D Pulse Mapping

Abstract

Background: A stringlike pulse is highly related to hypertension, and many classification approaches have been proposed in which the differentiation pulse wave (dPW) can effectively classify the stringlike pulse indicating hypertension. Unfortunately, the dPW method cannot distinguish the spring stringlike pulse from the stringlike pulse so labeled by physicians in clinics. Design: By using a Bi-Sensing Pulse Diagnosis Instrument (BSPDI), this study proposed a novel Plain Pulse Wave (PPW) to classify a stringlike pulse based on an array of pulse signals, mimicking a Traditional Chinese Medicine physician's finger-reading skill. Results: In comparison to PPWs at different pulse taking positions, phase delay Dhand correlation coefficient r can be elucidated as the quantification parameters of stringlike pulse. As a result, the recognition rates of a hypertensive stringlike pulse, spring stringlike pulse, and non-stringlike pulse are 100%, 100%, 77% for PPW and 70%, 0%, 59% for dPW, respectively. Conclusions: Integrating dPW and PPW can unify the classification of stringlike pulse including hypertensive stringlike pulse and spring stringlike pulse. Hence, the proposed novel method, PPW, enhances quantification of stringlike pulse.

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

“…First, the tonometer (PDS-2000) we applied in this study records only the two-dimensional data (pressure-time) of the pulse. Since more advanced instruments now available can obtain three dimensional data [ 61 ], further study should be undertaken. Second, although the phenomenological model we propose is capable of capturing IF and AM as well as the wave shape function, it is clearly not the optimal solution.…”

A review of wrist pulse analysis

“…In an unhealthy subject's tidal wave and dichotic wave amplitudes are very low. As static pressure increases, higher variations in pulse morphology were observed in unhealthy subjects compared to the healthy subjects [25]. Few research groups observed irregularities in the pulse waveform in the amplitudes and duration of tidal and dicrotic measured based on first order derivation [26].…”

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

“…Mechanotransduction is a biological process by which sensory cells convert biomechanical stimuli into cellular signals and significantly impact the development, regulation, and regeneration of organ functions. − Monitoring and measurement of vital biomechanical information such as blood pressure, cardiac pulsation, intraocular pressure, and articular movements are crucial in the early diagnosis of chronic and acute diseases and provide comprehensive assessments for tracking patient rehabilitation after reconstructive surgeries. − Moreover, perceiving external mechanical cues in the skin-mimetic approach facilitates restoring tactile sensation for injured people. − In fact, mechanical information from both the human body and external stimuli is usually aliased and accompanied by artifacts from inevitable movements (e.g., breathing, walking, and swallowing), whereby the transmitted data possesses serialized structure with variable amplitude, duration (pulse width), and frequency. Traditional methods that merely increase the sensitivity of sensors or use differential amplification circuits cannot precisely distinguish signals of interest (SOI) due to the indiscriminate enhancement or suppression of signals. , Although recent signal segmentation methods, such as bandpass filtering, independent and principal component analysis, can reduce the interference of noise on the SOI, they usually require specific prior information or complex iterative procedure, raising the computational cost and restricting the recognizable patterns .…”