Abstract:
A healthcare system is provided. The healthcare system includes a data server, an algorithm server, a display device, and a communication network. The data server stores a plurality of physiological signals. The algorithm server receives the plurality of physiological signals from the data server. The algorithm server applies a plurality of algorithms on the plurality of physiological signals to obtain at least one feature of the plurality of physiological signals and generates at least one label according to the at least one label. The display device displays the at least one label. The communication network communicatively connects the data server, the algorithm server, and the display device for providing signal transmission paths therebetween.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Application No. 62/261,900, filed on Dec. 2, 2015, the contents of which are incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    Field of the Invention 
         [0003]    The invention relates to a healthcare system, and more particularly to a healthcare system which can obtain labels for physiological signals. 
         [0004]    Description of the Related Art 
         [0005]    In some countries, such as India, 70% of the population lives in rural areas, but 3% of the total number of physicians in India practice there. Thus, a tele-health service was introduced to monitor the health of the people in the rural areas. The tele-health service is applied to obtain physiological signals from a patient (such as blood pressure, body temperature, heart rate, respiratory airflow and volume, oxygen saturation, and electrocardiography (ECG) signals) and transmits the physiological signals to a remote site for doctors through the network to make a diagnosis of a disease. The doctors may offer some feedback to the patient or local doctors for further treatment. Some physiological signals, such as ECG signals, need to be interpreted by cardiology specialists. However, there is a lack of cardiology specialists in India. If the ECG signals of all of the patients are transmitted to the cardiology specialists regardless of whether they suffer from cardiovascular diseases, the workload of the cardiology specialists will be very heavy and may result in inaccurate diagnosis of diseases. 
       BRIEF SUMMARY OF THE INVENTION 
       [0006]    An exemplary embodiment of a healthcare system, wherein the healthcare system comprises a data server, an algorithm server, a display device, and a communication network. The data server stores a plurality of physiological signals. The algorithm server receives the plurality of physiological signals from the data server. The algorithm server applies a plurality of algorithms on the plurality of physiological signals to obtain at least one feature of the plurality of physiological signals and generates a label according to the at least one feature. The display displays the label. The communication network communicatively connects the data server, the algorithm server, and the display device for providing signal transmission paths therebetween. 
         [0007]    Another exemplary embodiment of a monitoring method comprises the steps of obtaining a plurality of physiological signals; applying a plurality of algorithms on the plurality of physiological signals to obtain at least one feature of the plurality of physiological signals; generating a label according to the at least one feature; and showing the label. 
         [0008]    A detailed description is given in the following embodiments with reference to the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]    The invention can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein: 
           [0010]      FIG. 1  shows an exemplary embodiment of a healthcare system for physiological signals; 
           [0011]      FIG. 2  is a schematic block diagram showing data transmission in the healthcare system of  FIG. 1 ; 
           [0012]      FIG. 3  shows a flow chart of an exemplary embodiment of a monitoring method for physiological signals; 
           [0013]      FIG. 4  shows an exemplary embodiment of operations and algorithms of an algorithm server; 
           [0014]      FIG. 5  shows an example of a flat line appearing on an ECG signal; 
           [0015]      FIG. 6  shows an example of a sharp slop appearing on an ECG signal; 
           [0016]      FIG. 7  shows an example of high-frequency appearing on an ECG signal; 
           [0017]      FIG. 8  shows an example of a waveform of an ECG signal; 
           [0018]      FIG. 9  shows an example of relationship between an ECG signal and a vessel pulse signal; 
           [0019]      FIG. 10  shows an example of a heart axis; 
           [0020]      FIG. 11A  shows an example of a normal T-wave of an ECG signal; 
           [0021]      FIG. 11B  shows an example of a T-wave inversion of an ECG signal; 
           [0022]      FIG. 12A  shows an example of normal S and T-waves of an ECG signal; 
           [0023]      FIG. 12B  shows an example of an ST elevation of an ECG signal; 
           [0024]      FIG. 13  shows an example of a change of R-R intervals of an ECG signal in a period of time; and 
           [0025]      FIG. 14  shows an exemplary embodiment of a label list displayed on the display device of the healthcare system of  FIG. 1 . 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0026]    The following description is of the best-contemplated mode of carrying out the invention. This description is made for the purpose of illustrating the general principles of the invention and should not be taken in a limiting sense. The scope of the invention is best determined by reference to the appended claims. 
         [0027]      FIG. 1  shows an exemplary embodiment of a healthcare system for physiological signals. As shown in  FIG. 1 , a healthcare system  1  comprises a sensor device  10 , a data server  11 , an algorithm server  12 , and a display device  13 . The sensor device  10  detects at least one of the electrocardiography (ECG), photoplethysmogram (PPG), motion, body temperature, galvanic skin response, electroencephalograph, oxygen saturation, airflow in respiratory tract, heart rate, pulse wave transit time, and blood pressure of an object, such as a patient  15  shown in  FIG. 2 , and generates physiological signals S 10  in response to the detection result. After obtaining the physiological signals S 10 , the sensor device  10  transmits the physiological signals S 10  to the data server  11  through a communication network  14  shown in  FIG. 2 . The data server  11  receives the physiological signals S 10  for storage. In the embodiment, the data server  11  can collect and store physiological signals from different objects, such as different patients. The algorithm server  12  can issue a request RST to read the physiological signals S 10  stored in the data server  11  through the communication network  14 . When the algorithm server  12  receives the physiological signals S 10  of the patient  15  from the data server  11 , the algorithm server  12  applies algorithms on the received physiological signals S 10  to detect at least one feature of the physiological signals S 10  and obtain at least one label for the physiological signals S 10  according to the detected feature. In the following, one label is given as an example for illustration. The algorithm server  12  further generates a labeling result which comprises the information including the obtained label and detected feature. The display device  13  receives the labeling result S 12  from the algorithm server  12  through the communication network  14  and displays a label list according to the labeling result S 12 . The label list includes the obtained label for the physiological signals S 10 . A viewer, such as a doctor, can know what label is given to the physiological signals S 10 . 
         [0028]    In the embodiment, the label is classified into a not-screened-out category or a screened-out category. For example, the label may be an abnormal label, a normal label, or a noise label. An abnormal label is obtained for the physiological signals S 10  through the applied algorithms when the patient  15  suffers from diseases. A normal label is obtained for the physiological signals S 10  when the patient  15  does not suffer from diseases. A noise label is obtained for the physiological signals S 10  when the quality of the physiological signals S 10  is too low for a doctor to accept making a diagnosis of a disease. 
         [0029]    A doctor, such as a general physician or a cardiology specialist, can be aware of what label is obtained for the physiological signals S 10  according to the class of the features label and can thus make a decision to review the physiological signals S 10  or not. In the embodiment, the abnormal label is classified into the not-screened-out category, while the normal label and the noise label are classified into the screened-out category. For example, when a doctor is aware of what would be considered an abnormal label obtained for the physiological signals S 10 , the doctor can retrieve the physiological signals S 10  from the algorithm server  12  through the display device  13  in order to make a diagnosis of a disease. 
         [0030]    In the embodiment, the communication network  15  is implemented by a tele-communication network, Internet, LAN, wireless LAN, or any combinations thereof to transmit signals or data between the sensor device  10 , the data server  11 , the algorithm server  12 , and the display device  13 . Any two of the tele-communication network, Internet, LAN and wireless LAN may be connected via gateways. 
         [0031]    In the embodiment, the data server  11  can be implemented by dedicated-hardware that delivers database services or software executed by a processor, such as a general-purposed central processing unit (CPU), general-purposed graphics processing unit (GPU), micro-control unit (MCU), etc., for accomplishing the above operations. Similarly, the algorithm server  12  can be implemented by dedicated-hardware or software executed by a processor for accomplishing the above operations. 
         [0032]    In an embodiment, the sensor device  10  also transmits patient information to the data server  11  for storage, such as the name and age of the patient  15 . When the algorithm server  12  issues the request RST to the data server  11 , the data server  11  transmits not only the physiological signals S 10  but also the patient information to the algorithm server  12 . Accordingly, the information of the labeling result S 12  further comprises patient information, and the patient information can also be shown on the label list. In an embodiment, the labeling result S 12  comprises a string with a JSON format. 
         [0033]      FIG. 3  shows a flow chart of an exemplary embodiment of a monitoring method for physiological signals. The monitoring method will be described by referring to  FIG. 2  and  FIG. 1 . In the embodiment, electrocardiography (ECG) signals are given as an example for illustration. At the step S 30 , the sensor device  10  detects and obtains the ECG signals (physiological signals) of the patient  15 . The ECG signals are stored in the data server  11 . When receiving the ECG signals from the data server  11 , the algorithm server  12  applies algorithms on the received ECG signals S 10  to detect at least one feature of the ECG signals. The algorithm server  12  obtains a label for the ECG signals according to the detected feature (step S 31 ). The algorithm server  12  generates the labeling result S 12  to the display device  13 . The display device  13  determines the class of the label (step S 32 ). When the label is an abnormal label, it is classified into the not-screened-out category (step S 34 ). The display device  13  displays a label list to show the abnormal label (step S 36 ). As described above, the abnormal label indicates that the patient  15  may suffer from cardiovascular diseases. When a doctor, such as a general physician or a cardiology specialist, notes that there is an abnormal label on the label list which is classified into the not-screened-out category, the doctor may want to review the ECG signals to make a diagnosis of a disease. Thus, in cases where the doctor wants to review the ECG signals, the doctor issues a request through an input interference of the display device  15 , and the display device  15  can retrieve the ECG signals from the algorithm server  12  in response to the request and show the ECG signals (step S 36 ). When the label is a normal label or a noise label, it is classified into the screened-out category (step S 33 ). The display device  13  shows the abnormal label on the label list (step S 35 ). As described above, the normal label indicates that the patient  15  does not suffer from a disease and the noise label indicates that the quality of the ECG signals is too low. When the doctor notes that there is a normal label or a noise label on the label list which is classified into the screened-out category, the doctor is aware that the ECG signals can be ignored (step S 35 ). Thus, the display device  15  does not need to retrieve the ECG signals from the algorithm server  12 . 
         [0034]    According to the above embodiment, since the label list comprises the label of the ECG signals, the doctor can determine whether the patient  15  may suffer from any cardiovascular diseases according to the label. Accordingly, the doctor may simply review the waveforms of the ECG signals whose label is classified into the not-screened-out category but ignores the ECG signals whose label is classified into the not-screened-out category, thereby reducing the workload. In another embodiment, if necessary, the doctor can also issues a request to review the waveforms of the ECG signals whose label is classified into the screened-out category. 
         [0035]    In the above embodiment, one abnormal label is obtained for the physiological signals of the patient  15  who suffers from a disease. According to an embodiment, when the patient  15  suffers from diseases, several abnormal labels may be obtained for the physiological signals. Each abnormal label indicates one condition of a human body&#39;s organs. In the following, the detailed algorithms of the algorithm server  12  will be described by taking ECG signals as an example of the physiological signals. It has been known that ECG signals can represent the electrical activity of the human heart over a period of time by using ECG electrodes placed on the skin. For a conventional twelve-lead (12-lead) ECG, ten ECG electrodes are placed on the patient&#39;s limbs and on the surface of the chest. The overall magnitude of the heart&#39;s electrical potential is then measured from  12  different angles (“leads”) and is recorded over a period of time (usually several seconds). The twelve leads comprise I, II, III, aVL, aVR, aVF, V 1 , V 2 , V 3 , V 4 , V 5 , and V 6 , which serve as twelve ECG signals respectively. 
         [0036]      FIG. 4  shows an exemplary embodiment of the operations and algorithms of the algorithm server  12 . The embodiment will be described by referring to  FIGS. 2 and 4 . Referring to  FIG. 4 , when the algorithm server  12  receives the ECG signals of the patient  15  from the data server  11  (block  400 ), the algorithm server  12  discards the data of each of the ECG signals occurring in the first second (block  401 ), and then performs a noise removal algorithm to remove the noise of each ECG signals (block  402 ). After the noise of each ECG signal is removed, the algorithm server  12  applies a quality estimation algorithm on each ECG signal (block  402 ). 
         [0037]    In an embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects noise parameters of the ECG signal to estimate the quality of the ECG signal (block  402 A). When the algorithm server  12  estimates that the quality of the ECG signal is low according to the noise parameters, the ECG signal is not trustworthy for diagnosing diseases. In another embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects whether there is one flat line on the ECG signal or not (block  402 B). Referring to  FIG. 5 , detecting a flat line  50  appearing on the ECG signal indicates that the corresponding ECG electrodes are not placed on the right position or that no signal is detected by the corresponding ECG electrodes, and, thus, the algorithm server  12  estimates that the quality of the ECG signal is low. In another embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects whether there is a sharp slope on the ECG signal or not (block  402 C). Referring to  FIG. 6 , detecting a sharp slop  60  appearing on the ECG signal indicates that the patient  15  moves violently, and, thus, the algorithm server  12  estimates that the quality of the ECG signal is low. In another embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects whether there is a high-amplitude or high-frequency oscillation on the ECG signal or not (block  402 D). Referring to  FIG. 7 , detecting high-amplitude or high-frequency oscillation  70  appearing on the ECG signal indicates that there is an electronic apparatus, such as a television, mobile phone, or a motor, near the sensor detector  10  or the patient  15  and then the ECG signal is interfered with the signals from the electronic apparatus, and, thus, the algorithm server  12  estimates that the quality of the ECG signal is low. In another embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects whether there is a sharp baseline on the ECG signal or not (block  402 E). Detecting a sharp baseline appearing on the ECG signal indicates that the lines of the corresponding ECG electrodes are pulled or moved by the patient  15 , and, thus, the algorithm server  12  estimates that the quality of the ECG signal is low. In another embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects whether there is any data loss for the ECG signal or not (block  402 F). When the algorithm server  12  detects data loss for the ECG signal, the algorithm server  12  estimates that the quality of the ECG signal is low. In another embodiment, when the quality estimation algorithm is applied on each ECG signal, the algorithm server  12  detects whether there is any low voltage appearance for the whole ECG signal (block  402 G). Detecting a low voltage appearance for the whole ECG signal indicates that the quality of the sensor device  10  or ECG electrodes is low, and, thus, the algorithm server  12  estimates that the quality of the ECG signal is low. 
         [0038]    The above detection operations of the detection blocks  402 A- 402 G are examples for quality estimation. The algorithm server  12  can selectively perform at least one of the detection operations of the detection blocks  402 A- 402 G for accomplishing the quality estimation algorithm. In cases where the algorithm server  12  performs only one of the detection operations of the detection blocks  402 A- 402 B for each ECG signal to estimate the quality, when the detection result of the performed detection block indicates that the quality of the ECG signal is low, the ECG signal is treated as an ECG signal with noise. In cases where the algorithm server  12  performs some or all of the detection operations of the detection blocks  402 A˜ 402 G for each ECG signal, when the number of detection results of the detection blocks which indicate that the quality of the ECG signal is low exceeds a threshold, the ECG signal is treated as an ECG signal with noise. 
         [0039]    In an embodiment, one noise label is obtained for one ECG signal with low quality. For example, when the algorithm server  12  estimates that the quality of the ECG signal II is low, a noise label “LOW_QUALITY_II” is obtain for the ECG signal II. The noise label “LOW_QUALITY_II” will be shown on the label list on the display device  13 . In an embodiment, for 12-lead ECG (including twelve ECG signals), when the number of ECG signals with low quality exceeds a predetermine value or when the quality of the specific ECG signal(s) is estimated to be low, a noise label is obtained for the twelve ECG signals. For example, when the number of ECG signals with low quality exceeds 4 or when the quality of the ECG signals I, III, and aVF is estimated to be low, a noise label “Low_Quality_ECG” is obtained for the twelve ECG signals. The noise label “Low_Quality_ECG” will be shown on the label list on the display device  13 . 
         [0040]    After receiving the ECG signals of the patient  15 , the algorithm server  12  applies a feature extraction algorithm on the ECG signals (block  410 ). In an embodiment, the algorithm server  12  may detect beats of at least one ECG signal for obtaining the heart rate of the patient  15  (block  403 ). For example, as shown in  FIG. 8 , the algorithm server  12  detects the R-waves of the ECG signal I. Then, the algorithm server  12  applies a heart-rate algorithm to calculate the occurring frequency of the R-waves of the ECG signal I in a period of time to serve as the heart rate of the patient  15  (block  404 ). For example, the algorithm server  12  detects the R-waves of each of the twelve ECG signals. Then, the algorithm server  12  calculates occurring frequencies of the R-waves of the ECG signals in a period of time and calculates the average value or middle value of these occurring frequencies to serve as the heart rate of the patient  15 . 
         [0041]    In an embodiment, the algorithm server  12  may apply a waveform algorithm to extract ECG waveform features (block  405 ). For example, as shown in  FIG. 8 , for each ECG signal or at least one specific ECG signal, the algorithm server  12  extracts the waveform of the T-wave, the lowest level of the S-wave, and/or any other waveform feature which may be affected by cardiovascular diseases, such as the interval between the Q-wave and the T-wave (Q-T interval). The algorithm server  12  also extracts the interval of every two successive R-waves (R-R interval) of each ECG signal or at least one specific ECG signal. Each waveform feature may comprise multiple values. To make each feature more reliable, the proposed embodiment will calculate the middle value of the values of each waveform feature (block  406 ). 
         [0042]    In some embodiments, the sensor device  10  detects the ECG signals and the vessel pulse signal of the patient  15  at the same time. A sensor of the sensor device  10  contacts a specific region, such as the right wrist of the patient  15 . The sensor senses a vessel pulse waveform of the right wrist to generate the vessel pulse signal S 11 . The vessel pulse signal is also transmitted to the data server  11  for storage. When the algorithm server  12  issues the request RST to the data server  11 , the data server  11  transmits the ECG signals and the vessel pulse signal to the algorithm server  12 . Referring to  FIG. 9 , the algorithm server  12  calculates the time difference Dp-p between each peak of one ECG signal (such as the ECG signal I) and the peak of the vessel pulse signal S 90  following the peak of the ECG signal to serve as a waveform feature and calculates the middle value of the values of the time difference Dp-p in in a period of time T 90 . The time difference Dp-p is referred to as a pulse transmission time (PTT) which indicates the time period when the pressure wave of the blood pressure is output to the right wrist from the heart. The pulse transmission time is an index for possible risk of arterial stiffness. 
         [0043]    Moreover, the algorithm server  12  applies a heart-axis algorithm to determine the heart axis according to the ECG signals (block  407 ). When the algorithm server  12  averages all ECG signals, the direction of the average electrical depolarization can be indicated with an arrow (vector). The vector is the heart axis which is represented by a degree. A change of the heart axis or an extreme deviation can be an indication of pathology. Generally, a heart axis obtained from ECG signals of a healthy person is between −30° and 90° which is in the normal axis area shown in  FIG. 10 . 
         [0044]    After the ECG waveform feature, the pulse transmission time, and the heart axis are obtained, the algorithm server  12  performs a labeling algorithm (block  408 ). In an embodiment, according to the labeling algorithm, the algorithm server  12  detects whether there is a T-wave inversion or not on each ECG signal or a specific ECG signal according to the extracted polarity of the T-wave (block  408 A). For example, the algorithm server  12  detects whether there is a T-wave inversion or not on the ECG signal I according to the waveform of the T-wave. The waveform of the T-wave of the ECG signal I is one of the indexes for the possibility of myocardial infarction. Referring to  FIG. 11A , the waveform of the T-wave of the normal ECG signal I of a healthy person is positive. When the waveform of the T-wave of the normal ECG signal I is negative, the algorithm server  12  detects that there is a T-wave inversion on the ECG signal I, shown in  FIG. 11B , and gives an abnormal label “T-wave Inversion” or/and “Myocardial_Infarction” for the ECG signals of the patient  15 . 
         [0045]    In an embodiment, according to the labeling algorithm, the algorithm server  12  detects that there is an ST elevation or not on each ECG signal or a specific ECG signal according to the extracted lowest level of the S-wave (block  408 B). For example, the algorithm server  12  detects whether there is an ST elevation or not on the ECG signal I according to the extracted lowest level of the S-wave. The lowest level of the S-wave of the ECG signal I is one of the indexes for the possibility of myocardial injury. Referring to  FIG. 12A , the lowest level of the S-wave of the ECG signal I of a healthy person is a negative level or lower than the lowest level of the Q-wave. When the lowest level of the S-wave of the ECG signal I is a positive level or higher than the lowest level of the Q-wave or when the lowest level of the S-wave of the ECG signal I is higher than a reference level or a historical level of the same wave which is obtained at the previous detection, shown in  FIG. 12B , the algorithm server  12  detects that there is an ST elevation on the ECG signal I and gives an abnormal label “ST_Elevation” or/and “Myocardial_Injury” for the ECG signals of the patient  15 . 
         [0046]    In an embodiment, according to the labeling algorithm, the algorithm server  12  detects whether the patient  15  has hypertrophy according to the degree of the heart axis or not (block  408 C). The degree of the heart axis being between 90° and 180° indicates that the patient  15  may suffer from hypertrophy. When the degree of the heart axis is between 90° and 180°, which is in the right axis deviation area (RAD) as shown in  FIG. 10 , the algorithm server  12  gives an abnormal label “Right Axis Deviation” for the ECG signals of the patient  15  and may further give an abnormal label “Hypertrophy”. In another embodiment, other cardiovascular diseases may induce the right axis deviation, such as a left posterior fascicular block, lateral myocardial infarction, right ventricular hypertrophy, and ventricular ectopy. Thus, when the degree of the heart axis is in the right axis deviation area (90°˜180°), the algorithm server  12  may further give at least one of the abnormal labels “Left_Posterior_Fascicular_block”, “Lateral_Myocardial_Infarction”, “Right_Ventricular_Hypertrophy”, and “Ventricular_Ectopy”. 
         [0047]    In an embodiment, according to the labeling algorithm, the algorithm server  12  detects whether there is arrhythmia or not according to the interval of every two successive R-waves (R-R interval) of each ECG signal or at least one specific ECG signal extracted in the block  405  (block  408 D). The change of the R-R intervals of the ECG signals is one of the indexes for the possibility of arrhythmia. For example, the algorithm server  12  extracts the interval of every two successive R-waves (R-R interval) of the ECG signal I in the block  405  and then detects whether there is arrhythmia or not according to the extracted R-R intervals. Referring to  FIG. 13 , when the R-R intervals of the ECG signal I change in a period of time or when one of the R-R intervals of the ECG signal I (such as the R-R interval  130 ) is different from the others thereof, the algorithm server  12  detects that there is arrhythmia and gives an abnormal label “Arrhythmia” for the ECG signals of the patient  15 . 
         [0048]    Then, at the block  409 , the algorithm server  12  generates the labeling result S 12 , which comprises the information of the labels in the blocks  402  and  408 . The algorithm server  12  transmits the labeling result  12  to the display device  13  for displaying a label list. Accordingly, the labels obtained in the blocks  402  and  408  can be shown in the label list. 
         [0049]    In the above embodiment, when the algorithm server  12  does not give any abnormal label in the block  48 , a normal label “Normal_ECG” is obtained for the twelve ECG signals. 
         [0050]    In the above embodiment, the labels comprise at least one noise label, at least one abnormal label, and a normal label. According to another embodiment, the labels can further comprise at least one labels related to the heart information, such as the heart rate and the heart axis. For example, in the block  403 , the obtained heart rate is 74 bpm. The algorithm server  12  obtains a label “Heart_Rate” in the block  403 . Accordingly, the information of the labeling result S 12  further includes the label “Heart_Rate” and the information of the label “Heart_Rate” (that is 74 bpm). When the labeling result S 12  is transmitted to the display device  13 , the label “Heart_Rate” and the value “74 bpm” can also be shown on the label list. In an embodiment, after the algorithm server  12  determines the heart axis in the block  407 , the algorithm server  12  also obtains a label “Heart_Axis” at the same block. For example, the determined heart axis is 50°. Accordingly, the information of the labeling result S 12  further includes the label “Heart_Axis” and the information of the label “Heart Rate” (that is 50°). Through the transmission of the labeling result S 12 , the label “Heart_Axis” and the value “50°” can also be shown on the label list. 
         [0051]    Moreover, the algorithm server  12  can also obtain an abnormal label related to the level of the heart rate. In the block  403 , when the heart rate is obtained, the algorithm server  12  can determine whether the heart rate is higher than an upper threshold or whether the heart rate is lower than a lower threshold. When the algorithm server  12  determines that the heart rate is higher than the upper threshold, an abnormal label “Tachycardia” is provided for the ECG signals. When the algorithm server  12  determines that the heart rate is lower than the lower threshold, an abnormal label “Bradycardia” is provided for the ECG signals. Accordingly, the information of the labeling result S 12  further includes the label related to the level of the heart rate. Through the transmission of the labeling result S 12 , the label related to the level of the heart rate can also be shown on the label list. 
         [0052]    In an embodiment, the labels can further comprise a sleep stage label. It has been known that the heart rate of a human varies with the sleep stage. There are four sleep stages: an awake stage, a light sleep stage, a deep sleep stage, and a rapid eye movement sleep stage. In the block  403 , when the heart rate is obtained, the algorithm server  12  can obtain a sleep stage label according to the heart rate. Thus, the sleep stage label can be a label “AWAKE” for the awake stage, a label “Light_sleep” for the light sleep stage, a label “Deep-Sleep” for the deep sleep stage, and a label “Rapid_eye_movement_Sleep” for the rapid eye movement sleep stage. Accordingly, the information of the labeling result S 12  further includes the sleep stage label. Through the transmission of the labeling result S 12 , the sleep stage label can also be shown on the label list. 
         [0053]    In the embodiment, the labeling algorithm can be performed by using a learning-based algorithm, such as a decision tree, a nearest neighbor algorithm, a support vector machine (SVM) algorithm, a random forest algorithm, an AdaBoost algorithm, a Naïve Bayes algorithm, a Bayesian-network, a neural network, a clustering algorithm, and a deep learning algorithm. 
         [0054]    While the process flow described in  FIG. 4  includes a number of operations that appear to occur in a specific order, it should be apparent that these processes can include more or fewer operations, which can be executed serially or in parallel, e.g., using parallel processors or a multi-threading environment. 
         [0055]      FIG. 14  shows an exemplary embodiment of the label list displayed on the display device  13 . As shown in  FIG. 14 , the column “Record” lists the patient information, such as the patient&#39;s name, the column “Input Result” lists the information input by an interface of the display device  13 , and the column “Label” lists the labels and the information of the labels. According to the above embodiments, the abnormal label is classified into the not-screened-out category, while the normal label and the noise label are classified into the screened-out category. In the label list, the display device  13  displays the labels classified into the not-screened-out category from the feature levels classified into the screened-out category by different formats or colors; for example, plain text, text with a marker, text with highlighted contrast, and text with lowlighted contrast. In order to distinguish the feature levels classified into the not-screened-out category from the labels classified into the screened-out category, the labels classified into the screened-out category are represented by text with lowlighted contrast or text with deleting lines. The contents of the label list is determined by the labeling result S 12 , and the information of the labeling result S 12  is determined according to the results of the algorithms performed by the algorithm server  12 . 
         [0056]    In the above embodiment, the labels are obtained by the algorithm server  12  according to the extracted features of the ECG signals, such as the ECG waveform, the heart rate, the heart axis, and so on. In another embodiment, the display device  13  comprises an interface. A viewer, such as a doctor, can input a command through the interface to give a new label to the ECG signals or modify the original label. 
         [0057]    While the invention has been described by way of example and in terms of the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. On the contrary, it is intended to cover various modifications and similar arrangements (as would be apparent to those skilled in the art). Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.