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 Δθand 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.
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 Δθand 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.