TWI812285B - Method for detecting apnea based on heartbeat interval signals and autoregressive moving average model - Google Patents
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Abstract
Description
本發明係有關於一種睡眠呼吸功能障礙的偵測，特別是指一種為基於心跳間隔訊號與自迴歸移動平均模型之呼吸暫停事件偵測方法。The present invention relates to the detection of sleep breathing dysfunction, and in particular, to an apnea event detection method based on heartbeat interval signals and autoregressive moving average models.
查，阻塞睡眠呼吸暫停(Obstructive Sleep Apnea； 以下簡稱OSA)是一種常見且嚴重的睡眠呼吸功能阻礙，會在睡眠期間因咽部塌陷造成完全或部分上呼吸道阻塞，進而導致呼吸暫停或減弱，且根據先前的研究顯示，阻塞睡眠呼吸暫停與高血壓、冠心病、心律失常、心臟衰竭和中風的發病率有關，目前評估OSA嚴重程度的標準方法是進行睡眠多項生理檢查(Polysomnography；以下簡稱PSG)，受試者必須到睡眠實驗室或睡眠中心睡一個晚上，在護理人員的監督下，分別在頸部、眼角、下巴、心臟以及腿部貼上電極貼片，並且胸部及腹部套上感應帶，在手指套上血氧測量器，在口鼻套上呼吸感應器，在手臂上套上血壓計，以記錄整個晚上的睡眠生理數據，包括腦電圖、眼電圖、心電圖、下巴肌電圖、胸部呼吸訊號、腹部呼吸訊號、口鼻氣流、血氧濃度、血壓變化、心率，以及睡眠體位等，而PSG是結合呼吸氣流、胸部呼吸訊號、腹部呼吸訊號、以及血氧濃度來判斷並計算受試者每小時平均出現呼吸暫停(Apnea)與呼吸不足(Hypopnea)事件的次數(即呼吸暫停與呼吸不足指標; Apnea and Hypopnea Index (AHI))，藉以評估受試者OSA的嚴重程度，包括呼吸正常(Normal；AHI ＜5)、輕度OSA(Mild；AHI介於5到14)、中度OSA(Moderate；AHI介於15到30) 、以及嚴重OSA (Severe； AHI＞ 30)。According to the investigation, Obstructive Sleep Apnea (hereinafter referred to as OSA) is a common and serious sleep breathing disorder. It can cause complete or partial obstruction of the upper airway due to pharyngeal collapse during sleep, thus leading to apnea or weakening, and According to previous studies, obstructive sleep apnea is associated with the incidence of hypertension, coronary heart disease, arrhythmias, heart failure, and stroke. The current standard method to assess the severity of OSA is to perform polysomnography (hereinafter referred to as PSG). , the subjects must go to a sleep laboratory or sleep center to sleep for one night. Under the supervision of nursing staff, electrode patches are affixed to the neck, canthus of the eyes, chin, heart and legs, and induction belts are put on the chest and abdomen. , put a blood oxygen meter on your finger, a breathing sensor on your mouth and nose, and a blood pressure monitor on your arm to record sleep physiological data throughout the night, including electroencephalogram, electrooculogram, electrocardiogram, and chin electromyography Figure, chest breathing signal, abdominal breathing signal, mouth and nose airflow, blood oxygen concentration, blood pressure changes, heart rate, and sleeping position, etc., and PSG combines respiratory airflow, chest breathing signal, abdominal breathing signal, and blood oxygen concentration to judge and Calculate the average number of apnea (Apnea) and hypopnea (Hypopnea) events per hour in a subject (i.e., Apnea and Hypopnea Index (AHI)) to evaluate the severity of the subject's OSA. Including normal breathing (Normal; AHI <5), mild OSA (Mild; AHI between 5 and 14), moderate OSA (Moderate; AHI between 15 and 30), and severe OSA (Severe; AHI>30).
有鑒於PSG檢查的費用昂貴且不便，近年來便有人致力於研究使用量測較少的訊號來開發方便且花費少的呼吸暫停與不足事件偵測系統，主要被使用的訊號有血氧濃度、呼吸氣流、胸部與腹部呼吸訊號、心電圖、聲音訊號、以及結合不同的訊號；因為PSG主要是結合呼吸訊號(包括呼吸氣流、胸部呼吸與腹部呼吸)與血氧濃度來檢測呼吸暫停與呼吸不足事件，如果僅單獨使用呼吸氣流，胸部呼吸、腹部呼吸或血氣濃度時，是無法檢測所有的呼吸暫停呼吸不足事件，而更基於聲音訊號的檢測方法則受限於聲音訊號容易受到心臟聲音與環境噪音的干擾，但相較於單獨使用呼吸氣流、胸部呼吸訊號、血氧濃度、與聲音訊號，單導程心電圖則是一個能夠較好的反應出完整呼吸事件的訊號。In view of the expense and inconvenience of PSG examination, in recent years, some people have devoted themselves to research on using less measured signals to develop convenient and low-cost apnea and insufficient event detection systems. The main signals used include blood oxygen concentration, Respiratory airflow, chest and abdominal respiration signals, electrocardiogram, sound signals, and combination of different signals; because PSG mainly combines respiratory signals (including respiratory airflow, chest respiration and abdominal respiration) and blood oxygen concentration to detect apnea and hypopnea events , if you only use respiratory airflow alone, chest breathing, abdominal breathing or blood gas concentration, it is impossible to detect all apnea and hypopnea events, and detection methods based on sound signals are limited by the fact that sound signals are susceptible to heart sounds and environmental noise. interference, but compared to using respiratory airflow, chest respiratory signal, blood oxygen concentration, and sound signal alone, single-lead electrocardiogram is a signal that can better reflect the complete respiratory event.
請配合參閱圖1，在圖1中所顯示的是PSG所量測的呼吸氣流、胸部呼吸訊號、腹部呼吸訊號(圖中標示c)、心電圖訊號(圖中標示a)、以及PSG所提供的呼吸註記(準位0表示呼吸正常的期間，準位2表示呼吸暫停的期間，圖中標示d)，心跳間隔時間訊號(RR間隔訊號，圖中標示b)則是心電圖訊號中相鄰R波的間隔時間所組成的訊號，因此從圖1中可以觀察到呼吸暫停期間，心跳間隔時間訊號的變化緩慢，但是呼吸暫停結束之後，心跳間隔時間訊號明顯的減少且持續一段時間之後再恢復正常，是以，如果在原本正常平穩的心跳間隔時間訊號之後，持續出現一段心跳時間訊號的減少再恢復正常平穩的心跳間隔時間訊號，則代表出現一次呼吸暫停或呼吸不足事件，也稱為呼吸暫停與呼吸不足事件的心跳間隔時間變化模式；是以，要如何能有效快速偵測出被檢測者之病症所在，乃為本技術領域人員一致努力之目標。Please refer to Figure 1. What is shown in Figure 1 is the respiratory airflow, chest breathing signal, abdominal breathing signal (marked c in the figure), electrocardiogram signal (marked a in the figure) measured by PSG, and the PSG provided Respiration annotation (level 0 represents the period of normal breathing, level 2 represents the period of apnea, marked d in the figure), and the heartbeat interval signal (RR interval signal, marked b in the figure) is the adjacent R wave in the electrocardiogram signal Therefore, it can be observed from Figure 1 that during apnea, the heartbeat interval signal changes slowly, but after the apnea ends, the heartbeat interval signal significantly decreases and lasts for a period of time before returning to normal. Therefore, if after the original normal and stable heartbeat interval signal, the heartbeat interval signal continues to decrease for a period of time and then returns to the normal and stable heartbeat interval signal, it represents an apnea or hypopnea event, also known as apnea and apnea. The change pattern of heartbeat interval time of hypopnea event; therefore, how to effectively and quickly detect the disease of the subject is the goal of the concerted efforts of those in the technical field.
因此，本發明之目的，是在提供一種以基於心跳間隔訊號與自迴歸移動平均模型之呼吸暫停事件偵測方法，其可有效對呼吸暫停與不足事件出現時心跳間隔的變化進行估測與計算，藉以提升呼吸暫停或不足事件偵測的準確度。Therefore, the purpose of the present invention is to provide an apnea event detection method based on the heartbeat interval signal and the autoregressive moving average model, which can effectively estimate and calculate the changes in the heartbeat interval when apnea and insufficient events occur. , thereby improving the accuracy of apnea or hypopnea event detection.
於是，本發明呼吸暫停與不足事件之偵測方法，包含有輸入、轉換及估測等步驟；其中，該輸入步驟對欲分析的原始訊號進行1分鐘心跳間隔訊號的擷取與輸入，且由該轉換步驟透過一離散餘弦轉換先對該心跳間隔訊號進行轉換處理，其能使大部份該心跳間隔訊號的能量集中於低頻帶，接著再經該估測步驟以一自迴歸移動平均模型利用低階的模型來對轉換為低頻的心跳間隔訊號進行估測，以取得倒鐘形訊號後，最後對該倒鐘形訊號的深度與寬度的變化進行計算，且將計算出之數值與預設的門檻值進行比對，當深度與寬度均大於預設時，即表示偵測到呼吸不足與暫停事件，否則代表為呼吸正常事件；因此，先將1分鐘心跳間隔訊號轉換以使大部份能量集中於低頻帶，再利用該自迴歸移動平均模型具有估測特性，藉以取得倒鐘形訊號，並配合對該倒鐘形訊號的深度與寬度數值的計算，且與預設門檻值進行比對，如此即可有效比對中分類出偵測結果，進而有效提高對呼吸暫停與不足事件偵測的準確性。Therefore, the method for detecting apnea and insufficient events of the present invention includes the steps of input, conversion and estimation. Among them, the input step acquires and inputs the 1-minute heartbeat interval signal for the original signal to be analyzed, and uses The conversion step first converts the heartbeat interval signal through a discrete cosine transform, which can make most of the energy of the heartbeat interval signal concentrated in the low frequency band, and then uses an autoregressive moving average model through the estimation step. A low-level model is used to estimate the heartbeat interval signal converted into a low frequency to obtain the inverted bell-shaped signal. Finally, the changes in the depth and width of the inverted bell-shaped signal are calculated, and the calculated values are compared with the default values. The threshold value is compared. When the depth and width are both greater than the preset, it means that the event of hypopnea and pause is detected, otherwise it means that the event of normal breathing is detected. Therefore, the 1-minute heartbeat interval signal is first converted to make most of the The energy is concentrated in the low frequency band, and then the autoregressive moving average model has estimation characteristics to obtain an inverted bell-shaped signal, and the depth and width values of the inverted bell-shaped signal are calculated and compared with the preset threshold. Yes, in this way, the detection results can be effectively classified through comparison, thereby effectively improving the accuracy of detecting apnea and deficiency events.
有關本發明之前述及其他技術內容、特點與功效，在以下配合參考圖式之較佳實施例的詳細說明中，將可清楚的明白。The aforementioned and other technical contents, features and effects of the present invention will be clearly understood in the following detailed description of the preferred embodiments with reference to the drawings.
參閱圖2，本發明一較佳實施例，一種呼吸暫停與不足事件之偵測方法，包含有一輸入步驟，一轉換步驟以及一估測步驟等步驟；其中，在該輸入步驟中對欲分析之原始訊號進行1分鐘心跳間隔訊號的擷取並進行輸入，而前述該原始訊號為受測者接受心電圖機之量測記錄所得的心跳訊號。Referring to Figure 2, a preferred embodiment of the present invention, a method for detecting apnea and insufficient events, includes an input step, a conversion step and an estimation step; wherein in the input step, the data to be analyzed is The original signal is to capture and input the 1-minute heartbeat interval signal, and the aforementioned original signal is the heartbeat signal measured and recorded by the electrocardiograph.
仍續上述，在該轉換步驟中，利用一離散餘弦轉換(Discrete Cosine Transform; DCT)，該離散餘弦轉換是為一可分離的轉換，其轉換核為餘弦函數，且除具有一般的正交轉換性值外，還具有很強能量集中特性，以及具有較弱邊界效應特性，以降低函式的要求來簡化運算，因此離散餘弦轉換得以對輸入之該心跳間隔訊號的大部份能量集中於低頻帶進行轉換的處理，使該心跳間隔訊號形成為低頻的訊號形態；至該估測步驟則利用一自迴歸移動平均(Autoregressive Moving Average; ARMA)模型，該自迴歸移動平均模型則是具有對時間序列進行零均值平穩化處理，以及變形時間序列的預測的特性，以使該自迴歸移動平均模型以低階的模型模式對經轉換為低頻的心跳間隔訊號進行估測，且在估測過程中取得倒鐘形訊號，並再以一演算法則來配合對該倒鐘形訊號的深度與寬度進行計算，以將計算數值與一預設門檻值進行比對，而前述該自迴歸移動平均模型採用的演算法則為一反覆式最小誤差平方演算法則(Iterative Least Square Error Algorithm)，來對該倒鐘形訊號進行估測計算，即為當計算出該倒鐘形訊號的深度與寬度均大於預設時，即表示偵測到呼吸不足與暫停事件，否則代表為呼吸正常事件。Continuing with the above, in this conversion step, a discrete cosine transform (Discrete Cosine Transform; DCT) is used. The discrete cosine transform is a separable transformation, and its transformation kernel is a cosine function, and in addition to the general orthogonal transformation In addition to the characteristic value, it also has strong energy concentration characteristics and weak boundary effect characteristics, which reduces the requirements of the function and simplifies the operation. Therefore, the discrete cosine transform can concentrate most of the energy of the input heartbeat interval signal into a low The frequency band is converted to form the heartbeat interval signal into a low-frequency signal form; in the estimation step, an Autoregressive Moving Average (ARMA) model is used, and the Autoregressive Moving Average (ARMA) model has the function of time The sequence is subjected to zero-mean smoothing processing and the prediction characteristics of the deformed time series enable the autoregressive moving average model to estimate the heartbeat interval signal converted to low frequency in a low-order model mode, and during the estimation process Obtain the inverted bell-shaped signal, and then use an algorithm to calculate the depth and width of the inverted bell-shaped signal to compare the calculated value with a preset threshold, and the aforementioned autoregressive moving average model uses The algorithm is an iterative least square error algorithm (Iterative Least Square Error Algorithm) to estimate and calculate the inverted bell-shaped signal, that is, when the depth and width of the inverted bell-shaped signal are calculated to be greater than the preset When , it means that the event of hypopnea and pause is detected, otherwise it means that the event of normal breathing is detected.
參閱圖2及圖3，進行偵測時，首先輸入1分鐘的心跳間隔訊號，而後先藉由該離散餘弦轉換具有很強能量集中特性，以對輸入之該心跳間隔訊號s(n)進行處理，使該離散餘弦轉換得以將大部份的心跳間隔訊號的能量集中於低頻帶的轉換，再由該自迴歸移動平均模型以低階的模型對轉換為低頻的心跳間隔訊號進行估測，以取得倒鐘形訊號，即為假設 x( n)的長度為 N， n＝0,1,2,Λ, N－1，而DCT的方程式如下： Referring to Figure 2 and Figure 3, during detection, a 1-minute heartbeat interval signal is first input, and then the input heartbeat interval signal s(n) is processed through the discrete cosine transform, which has strong energy concentration characteristics. , so that the discrete cosine transform can concentrate most of the energy of the heartbeat interval signal in the low-frequency band, and then the autoregressive moving average model uses a low-order model to estimate the converted heartbeat interval signal to To obtain an inverted bell-shaped signal, it is assumed that the length of x ( n ) is N , n = 0,1,2,Λ, N -1, and the equation of DCT is as follows:
其中 x(n)表示時域中整個QRS區間的訊號， X( k)為DCT領域中的DCT係數，而 X( k)的反向離散餘弦轉換(Inverse Discrete Cosine Transform; IDCT)定義如下： Where x(n) represents the signal of the entire QRS interval in the time domain, X ( k ) is the DCT coefficient in the DCT field, and the inverse discrete cosine transform (IDCT) of X ( k ) is defined as follows:
藉由在為該離散餘弦轉換領域中建立ARMA(2,2)模型，且這個模型具有一對共軛極點 (r ∠ θ)及兩個實數零點( a 1與 a 2)，其轉移函數定義如下： By establishing an ARMA(2,2) model in the discrete cosine transform field, and this model has a pair of conjugate poles (r ∠ θ) and two real zeros ( a 1 and a 2 ), its transfer function is defined as follows:
其中K為轉移函數的增益值， ， a= K － c， b=2cr cosθ －( a 1＋ a 2) K，而該ARMA(2,2)模型的脈衝響應為一組振幅遞減的弦波，具體而言，經由該IDCT轉換後將在時域中可以得到一組鐘形的訊號，在本實施例中用來輸入進行估測的心跳間隔訊號中是否出呼吸不足與暫停時所產生心跳變化(即心跳間隔時間減少且持續一段時間之後再恢復正常)；因此，在該DCT領域中，該ARMA(2,2)模型在最佳化的條件之下，可產生 X(k)估測訊號 X’( k)， X’( k)再經由該IDCT轉換之後即可得到心跳間隔訊號的 x( n)估測訊號 x’( n)，而E( k)是該ARMA(2,2)模型的估測誤差，且值得注意的是，配合計算是採用該反覆式最小誤差平方演算法則來估測該ARMA(2,2)模型的最佳參數，包括A 1,A 2,B 0,B 1,B 2。 where K is the gain value of the transfer function, , a = K - c , b=2cr cosθ - ( a 1 + a 2 ) K , and the impulse response of the ARMA(2,2) model is a set of sinusoidal waves with decreasing amplitude. Specifically, through the IDCT conversion Later, a set of bell-shaped signals will be obtained in the time domain. In this embodiment, the heartbeat interval signal is used to input and estimate whether there are heartbeat changes caused by hypopnea and pause (that is, the heartbeat interval decreases and continues. Return to normal after a period of time); therefore, in the DCT field, the ARMA(2,2) model can generate X(k) estimation signals X' ( k ), X' under optimized conditions ( k ) After the IDCT conversion, the x ( n ) estimated signal x' ( n ) of the heartbeat interval signal can be obtained, and E( k ) is the estimation error of the ARMA(2,2) model, and is worth Note that the coordination calculation uses the iterative minimum error square algorithm to estimate the optimal parameters of the ARMA(2,2) model, including A 1 , A 2 , B 0 , B 1 , and B 2 .
請配合參閱圖4及圖5，在圖4中所示之黑色線是輸入1分鐘心跳間隔訊號，而紅色線是經該IDCT轉換後的該ARMA(2,2)模型估測訊號 x’( n)，而在圖5中所示之黑色線是經該DCT轉換後的心跳間隔訊號 X(k)，紅色線則是該ARMA(2,2)模型所估測訊號 X’( k)，因此在圖5中可以明顯看出，低階的該ARMA(2,2)模型可以在該DCT領域中估測出大部份的心跳間隔訊號，再進一步從該圖4中則更可觀察到當輸入的心跳間隔訊號有出呼吸暫停與不足時的變化時(即心跳間隔時間減少且持續一段時間之後再恢復正常)，該ARMA(2,2)模型的輸出訊號是呈現一個倒鐘形訊號，而當這個倒鐘形訊號呈現出的深度與寬度為夠深、夠寬時，則代表所輸入的心跳間隔訊號出現了呼吸暫停與不足時所產生的心跳間隔變化，即如圖6所示，其圖中顯示出倒鐘形訊號的深度與寬度計算，本實施例定義這個倒鐘形訊號的深度與寬度如下： Please refer to Figure 4 and Figure 5 together. The black line shown in Figure 4 is the input 1-minute heartbeat interval signal, and the red line is the ARMA(2,2) model estimated signal x' ( n ), and the black line shown in Figure 5 is the heartbeat interval signal X(k) converted by the DCT, and the red line is the signal X' ( k ) estimated by the ARMA(2,2) model, Therefore, it can be clearly seen in Figure 5 that the low-level ARMA(2,2) model can estimate most of the heartbeat interval signals in the DCT field. It can be further observed from Figure 4 When the input heartbeat interval signal changes from apnea to insufficiency (that is, the heartbeat interval decreases and lasts for a period of time before returning to normal), the output signal of the ARMA(2,2) model presents an inverted bell-shaped signal. , and when the depth and width of this inverted bell-shaped signal are deep enough and wide enough, it means that the input heartbeat interval signal has apnea and insufficient heartbeat interval changes, as shown in Figure 6 , the figure shows the calculation of the depth and width of the inverted bell-shaped signal. This embodiment defines the depth and width of the inverted bell-shaped signal as follows:
倒鐘形訊號的深度=Min(RR(P1)RR(P2))－RR(P3)Depth of inverted bell signal =Min(RR(P1)RR(P2))-RR(P3)
其中RR(Pn)表示點Pn的RR值，Min函數表示取最小值。Among them, RR(Pn) represents the RR value of point Pn, and the Min function represents the minimum value.
倒鐘形訊號的寬度=Time(A1)－Time(A2)The width of the inverted bell signal = Time(A1)-Time(A2)
其中點A1與點A2是倒鐘形訊號與虛線L1的交會點，虛線L1的RR值= Min(RR(P1)RR(P2))－倒鐘形訊號的深度＊0.1。Points A1 and A2 are the intersection points of the inverted bell-shaped signal and the dotted line L1. The RR value of the dotted line L1 = Min(RR(P1)RR(P2)) - the depth of the inverted bell-shaped signal * 0.1.
由此可知，當所估測出該倒鐘形訊號的深度與寬度均大於預設的門檻值時，則表示偵測到輸入的1分鐘RR間隔訊號中出現呼吸不足與暫停時所產生的心跳間隔變化(即心跳間隔時間減少且持續一段時間之後再恢復正常)，因此便可表示所輸入的1分鐘的心跳間隔訊號已被偵測到有呼吸不足與暫停事件，反之，若該倒鐘形訊號的深度與寬度均小於預設的門檻值時，其便可表示所輸入的1分鐘的心跳間隔訊號並未被偵測到有呼吸不足與暫停事件；是以，先將1分鐘心跳間隔訊號轉換以使大部份能量集中於低頻帶中，再利用該自迴歸移動平均模型具有估測特性，有效取得倒鐘形訊號，並配合對該倒鐘形訊號的深度與寬度數值的計算，且與預設門檻值進行比對，如此即可有效比對分類出偵測結果，進而達到提高對呼吸暫停與不足事件偵測的準確性。It can be seen from this that when the estimated depth and width of the inverted bell-shaped signal are both greater than the preset threshold, it means that the heartbeat generated when hypopnea and pause occur in the input 1-minute RR interval signal is detected. Interval changes (that is, the heartbeat interval decreases and lasts for a period of time before returning to normal), so it can mean that the input heartbeat interval signal of 1 minute has been detected with hypopnea and pause events. On the contrary, if the inverted bell-shaped When the depth and width of the signal are both less than the preset threshold, it means that the input 1-minute heartbeat interval signal has not detected hypopnea and pause events; therefore, the 1-minute heartbeat interval signal is first Convert so that most of the energy is concentrated in the low frequency band, and then use the estimation characteristics of the autoregressive moving average model to effectively obtain the inverted bell-shaped signal, and cooperate with the calculation of the depth and width values of the inverted bell-shaped signal, and Compare with the preset threshold value, so that the detection results can be effectively compared and classified, thereby improving the accuracy of detecting apnea and insufficient events.
歸納前述，本發明基於心跳間隔訊號與自迴歸移動平均模型之呼吸暫停事件偵測方法，其針對受測者接受心電圖機之量測記錄所得的1分鐘心跳間隔訊號進行偵測，即利用輸入、轉換及估測等步驟的進行，即先以離散餘弦轉換對心跳間隔訊號進行轉換，以使大部份的心跳間隔訊號的能量集中於低頻帶後，再利用自迴歸移動平均模型以低階的模型對轉換為低頻的心跳間隔訊號進行估測，以取得倒鐘形訊號，並計算該倒鐘形訊號的深度與寬度，以將計算數值與預設門檻值進行比對，即可有效比對中分類出偵測結果，以提高偵測呼吸暫停與不足事件的準確性。To summarize the above, the present invention is based on the apnea event detection method of the heartbeat interval signal and the autoregressive moving average model. It detects the 1-minute heartbeat interval signal obtained by the subject's measurement and recording by the electrocardiograph, that is, using input, The conversion and estimation steps are carried out, that is, the heartbeat interval signal is first converted by discrete cosine transform, so that most of the energy of the heartbeat interval signal is concentrated in the low frequency band, and then the autoregressive moving average model is used to convert the heartbeat interval signal into a low-order The model estimates the heartbeat interval signal converted to low frequency to obtain an inverted bell-shaped signal, and calculates the depth and width of the inverted bell-shaped signal to compare the calculated value with the preset threshold for effective comparison. The detection results are classified into categories to improve the accuracy of detecting apnea and hypopnea events.
惟以上所述者，僅為說明本發明之較佳實施例而已，當不能以此限定本發明實施之範圍，即大凡依本發明申請專利範圍及發明說明書內容所作之簡單的等效變化與修飾，皆應仍屬本發明專利涵蓋之範圍內。However, the above descriptions are only for illustrating the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, that is, simple equivalent changes and modifications may be made based on the patent scope of the present invention and the contents of the description of the invention. , should still fall within the scope covered by the patent of this invention.
(本發明) 無(this invention) None
圖1是習知呼吸訊號、呼吸註記、心電圖與心跳間隔時間圖例之示意圖。 圖2本發明之一較佳實施例之流程圖。 圖3是離散餘弦轉換與自迴歸移動平均模型用於估測心跳間隔訊號的方塊示意圖。 圖4是心跳間隔訊號 x( n)與IDCT轉換後的ARMA(2,2)模型估測訊號 x’( n)的示意圖。 圖5是經DCT轉換後的心跳間隔訊號 X( k)與ARMA(2,2)模型的估測訊號 X’( k)的示意圖。 圖6是倒鐘形訊號的深度與寬度計算的示意圖。 Figure 1 is a schematic diagram of a conventional illustration of respiratory signals, respiratory notes, electrocardiogram and heartbeat interval time. Figure 2 is a flow chart of a preferred embodiment of the present invention. Figure 3 is a block diagram of discrete cosine transform and autoregressive moving average models used to estimate heartbeat interval signals. Figure 4 is a schematic diagram of the heartbeat interval signal x ( n ) and the ARMA (2,2) model estimated signal x' ( n ) after IDCT conversion. Figure 5 is a schematic diagram of the heartbeat interval signal X ( k ) converted by DCT and the estimated signal X' ( k ) of the ARMA (2,2) model. Figure 6 is a schematic diagram of the depth and width calculation of the inverted bell-shaped signal.
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