| 摘要: | 男性在五十歲後，下泌尿道症狀(Lower Urinary Tract Symptoms, LUTS)盛行率約為50～75%，且隨年齡增加而上升。下泌尿道症狀常見成因包含逼尿肌過動、膀胱功能低下與膀胱出口阻塞等。臨床上，尿路動力學為評估排尿功能的重要工具，可量測排尿量、最大與平均排尿流速以及排尿時間等參數，協助醫師判斷排尿狀態。然而診斷方式時常需於醫療院所等封閉式環境進行，或以侵入式方法進行診斷，患者在非自然的環境中接受長時間測試，易產生不適與心理壓力，且檢測結果可能因環境與情緒因素與日常排尿狀況有所差異，導致無法完全反映患者真實情況。
為克服上述限制，研究以智慧型手機為診斷平台，建立一套遠端、非侵入式之等效尿流速量測系統。利用手機麥克風錄製尿液撞擊水面聲音，以標準尿流速計作為參考，針對平坦型、滴尿型、斷續型與中斷型四種排尿型態建立等效流速估測算法。量測流程以高通濾波去除低頻環境干擾，並計算聲音訊號之根均方值(Root Mean Square, RMS)曲線；利用頻譜分析選定截止頻率之低通濾波與移動平均平滑方法，使RMS曲線在訊號波形起伏趨勢貼近標準尿流速曲線。透過面積比例法，根據已知排尿總量將聲音訊號轉換為尿流速(ml/s)，完成由音量訊號估測尿流速曲線的等效轉換。考量遠端居家診斷環境，研究建立寬頻衝擊噪音、窄頻衝擊噪音與風扇穩態噪音三種模擬嘈雜環境，探討噪音對尿流音量測之干擾及對診斷指標的影響。利用變分模態分解(Variational Mode Decomposition, VMD)將混合訊號分解為多個窄頻本質模態函數(Intrinsic Mode Function, IMF)，結合分段皮爾森相關係數與能量占比構成之自適應評分函數，自適應挑選最具尿流生理特徵之目標 IMF，以實現單通道盲源分離。
本研究於理想環境中，針對四種排尿型態各蒐集十五筆模擬排尿資料，新舊手機皆可重建與標準尿流速計高度相關流速曲線。針對臨床指標，排尿時間估測之決定係數達0.999(新手機)與0.995(舊手機)；最大尿流速估測之決定係數介於0.79～0.85，所有樣本之決定係數約為0.82，顯示本研究所建立之演算程序在排尿起止判定及峰值流速追蹤上皆具良好解釋力。針對曲線相似度評估，以對稱平均絕對百分比誤差(Symmetric Mean Absolute Percentage Error, SMAPE)作為指標，顯示平坦型態因流速變化平順，誤差最低；滴尿與斷續型在高頻起伏誤差較高，但整體波形趨勢仍與標準曲線相符。結果證實本研究之流速估測算法具有跨裝置穩健性，可減少不同手機規格對估測結果的影響。於噪音環境中，未經處理之含噪音訊號往往導致排尿時間被高估或低估，且最大流速峰值被衝擊聲或風扇背景噪音掩蓋。藉由VMD及自適應IMF判定，寬頻、窄頻衝擊噪音與風扇噪音均可被有效去除，模態判定的平均準確率達94.2%，標準差約2.25。多數訊號於降噪後，其流速曲線SMAPE誤差均顯著下降，最大流速與排尿時間之估測可回復至接近理想環境下的準確度，僅少數風扇噪音訊號因模態判定錯誤而未改善，顯示在不同噪音類型與不同排尿型態下，自適應判定方法仍能保持穩定且高準確度的判斷表現。
綜上所述，本研究建立一套以聲音為基礎之遠端等效尿流速量測系統，不僅在理想環境下提供與標準尿流速計相當的流速曲線與排尿指標，亦能在模擬居家噪音環境中維持高準確度之流速估測與噪音抑制能力，能夠作為傳統尿流速計之等效替代方案。
;The prevalence of lower urinary tract symptoms (LUTS) in men over the age of 50 is approximately 50–75% and increases with age. Common causes of LUTS include detrusor overactivity, impaired bladder function, and bladder outlet obstruction. Clinically, urodynamic studies are an important tool for evaluating urinary function. These tests can measure parameters such as voided volume, maximum and average urinary flow rate, and voiding time, helping physicians assess a patient′s urination condition. However, diagnosis often requires testing in controlled clinical environments or through invasive procedures. These settings can be unnatural and lengthy, causing discomfort and psychological stress to the patient. Additionally, results obtained under such conditions may differ from daily urination patterns due to environmental and emotional factors, potentially failing to fully reflect the patient’s actual condition.
To overcome these limitations, this study proposes the use of a smartphone-based platform to establish a remote, non-invasive urinary flow rate estimation system. The system records the sound of urine striking the water surface using the smartphone microphone. Using a standard uroflowmetry as a reference, the study developed estimation algorithms for four types of urination patterns: plateau-type, dribbling-type, intermittent-type, and interrupted-type. The measurement process includes high-pass filtering to eliminate low-frequency ambient noise and calculating the root mean square (RMS) of the audio signal. Spectrum analysis is used to determine the cutoff frequency for low-pass filtering and to apply a moving average smoothing method so that the RMS curve closely follows the trend of the standard uroflow curve. Through an area ratio method and known total urine volume, the audio signal is converted into urinary flow rate (ml/s), completing the equivalent transformation of volume signals into a flow rate curve. Considering remote home diagnostic environments, the study simulated three types of noisy conditions: broadband impact noise, narrowband impact noise, and fan-like steady-state noise, to investigate their effects on audio-based flow rate estimation and diagnostic indicators. Variational Mode Decomposition (VMD) was employed to decompose the mixed signals into several narrowband intrinsic mode functions (IMFs). An adaptive scoring function, combining segmented Pearson correlation coefficients and energy ratios, was used to select the target IMF with the most relevant urinary flow features, achieving single-channel blind source separation.
In an ideal environment, fifteen simulated urination samples for each of the four urination types were collected. The results showed that both new and old smartphones could reconstruct flow rate curves highly correlated with those from standard uroflowmetry. Regarding clinical indicators, the coefficient of determination (R²) for estimating voiding time reached 0.998 (new phones) and 0.995 (old phones). For maximum flow rate, R² ranged from 0.79 to 0.85, with an overall average of around 0.82, demonstrating the algorithm’s strong performance in identifying the start and end of voiding as well as tracking peak flow rates. For curve similarity evaluation, the symmetric mean absolute percentage error (SMAPE) was used. Plateau-type flows, with smoother rate changes, had the lowest errors. Dribbling and intermittent types had higher errors due to high-frequency fluctuations, but the overall waveform trends remained consistent with the standard curves. These results confirm that the developed estimation algorithm is robust across devices, reducing the impact of varying smartphone hardware on the results.
In noisy environments, unprocessed audio signals often led to over- or underestimation of voiding time, and maximum flow rate peaks were masked by impact or fan noise. Using VMD and adaptive IMF selection, all types of simulated noise were effectively suppressed. The average accuracy of IMF mode identification reached 94.2%, with a standard deviation of approximately 2.25. After noise reduction, most samples showed significantly decreased SMAPE errors in their flow rate curves. The estimations for maximum flow rate and voiding time returned to near-ideal accuracy, with only a few fan noise samples remaining uncorrected due to incorrect mode identification. This demonstrates that the adaptive method maintained stable and high-accuracy performance across different noise types and urination patterns.
In conclusion, this study developed a remote acoustic-based equivalent urinary flow measurement system. It not only provides flow rate curves and urination indicators comparable to standard uroflowmetry in ideal settings, but also maintains high accuracy in simulated home noise environments. This system has the potential to serve as an effective alternative to conventional uroflowmetry. |
