This invention relates to electrocardiogram monitoring. In particular, this invention relates to body sensor networks that record and transmit an electrocardiogram with reduced data storage and energy consumption requirements.
An electrocardiogram (ECG) is a time-varying signal representing the electrical activity of the heart, and is an effective, non-invasive diagnostic tool for cardiac monitoring. Recently, several systems have been developed for continuous, remote ECG monitoring using Body Sensor Networks (BSNs). Such systems typically consist of a wireless, battery-operated, body-worn sensor that collects ECG data and transmits it to a gateway device such as a smartphone. The gateway reports this data over the internet to a remote base station, which is typically a hospital server or caregiver's computer. Such remote monitoring allows collection of data during a person's daily routine and enables early detection of conditions such as tachycardia or angina. Further, the availability of continuous long-term data can help identify gradual, long-term trends in the cardiac health of at-risk patients.
A key challenge in BSN-based ECG monitoring is the large volume of data collected by the sensor in a short time interval. For example, at a clinically-recommended sampling rate of 250 Hz and resolution of 12 bits/sample, more than 2 KB of data is collected within 6 seconds. Local storage of this data on the sensor or the gateway device is impractical due to storage limitations. Further, wireless transmission of this data consumes significant power at the energy-constrained sensor. At the same time, the quality and continuity of the reported ECG signal must be maintained at the base station to allow effective investigation and diagnosis by a physician.
Most current attempts to address these key challenges are based on data compression, where the sensed ECG data is compressed before transmission. Several techniques based on wavelets, Huffman coding and priority-based encoding have been proposed in literature. Unfortunately, known compression schemes need to continuously transmit data, thus limiting their energy savings. In one alternative approach, a set of features is extracted from the sensed ECG and used for classification. The preprocessing and pattern recognition workload is transferred to local nodes close to the ECG leads to reduce transmission energy consumption. This scheme, however, does not provide a complete sensed ECG signal at the base station and thus its value for diagnosis is limited. Another compressive sensing approach has been proposed for ECG monitoring, which uses the sparsity of the ECG signal in specific wavelet transformations to reduce sampling rate. However, reconstruction of the received signal is complex and strongly depends on error-free transmission of all coefficients.
The present invention provides methods for monitoring an electrocardiogram. In one embodiment, the methods include receiving a sensed ECG signal from one or more sensors configured to collect the sensed ECG signal from the patient, comparing the sensed ECG signal to a model ECG signal, and, if a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, transmitting deviation data describing the deviation to a base station.
In another embodiment, the methods include receiving, at a sensor platform, a sensed ECG signal from one or more sensors configured to collect the sensed ECG signal from the patient and comparing, at the sensor platform, the sensed ECG signal to a model ECG signal. If a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, the methods may further include transmitting, from the sensor platform, deviation data describing the deviation to a base station. The methods may further include generating, at the base station, an output ECG signal to be displayed on a display device, wherein the output ECG signal comprises the model ECG signal and, when deviation data is received, further comprises a modification to the model ECG signal.
The present invention further provides systems for monitoring an ECG. In one embodiment, the system is a body sensor network for monitoring an electrocardiogram of a patient. The body sensor network may include a base station comprising a base station module configured to generate an ECG model and to generate an output ECG signal for displaying on a display device, and a sensor platform in electrical communication with the base station. The sensor platform may have a sensor platform module configured to: receive a sensed ECG signal from one or more sensors attached to the patient and collecting the patient's ECG embodied in the sensed ECG signal; receive an instance of the ECG model and produce a model ECG signal from the instance; compare the sensed ECG signal to the model ECG signal; and, if a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, transmit deviation data describing the deviation to the base station module. The sensor platform module does not transmit the sensed ECG signal if there is no deviation of the sensed ECG signal from the model ECG signal exceeding the threshold.
The following discussion is presented to enable a person skilled in the art to make and use embodiments of the invention. Various modifications to the illustrated embodiments will be readily apparent to those skilled in the art, and the generic principles herein can be applied to other embodiments and applications without departing from embodiments of the invention. Thus, embodiments of the invention are not intended to be limited to embodiments shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein. The following detailed description is to be read with reference to the figures, in which like elements in different figures have like reference numerals. The figures, which are not necessarily to scale, depict selected embodiments and are not intended to limit the scope of embodiments of the invention. Skilled artisans will recognize the examples provided herein have many useful alternatives and fall within the scope of embodiments of the invention.
As used herein, the singular forms “a”, “an” and “the” include plural referents unless the context clearly dictates otherwise. “And” as used herein is interchangeably used with “or” unless expressly stated otherwise. All embodiments of the invention can be combined unless the context clearly dictates otherwise.
The ECG signal bas been extensively studied and used for cardiac diagnosis. The basic unit of an ECG is a beat, and its shape is referred to as the ECG morphology. Referring to
The distance between two consecutive R peaks is called the R-R interval, and its reciprocal gives the instantaneous heart rate. Even in a healthy person, the R-R interval varies across beats due to several physiological factors. This variation is described using temporal features such as mean and standard deviation of heart rate, and spectral features such as Low Frequency/High Frequency (LF/HF) ratio. The temporal and spectral features of the ECG are referred to herein as interbeat features.
ECG is inherently a low amplitude electrical signal and is often corrupted by noise from various sources such as electrical mains, muscle noise and patient movement or respiration. As a result, the measured signal must be filtered as described below to extract the underlying ECG waveform. Among the constituent waves, the QRS complex can be extracted using computationally lightweight algorithms. The extraction of P and T waves, however, requires advanced filtering techniques that are computationally expensive to implement on sensors. Further, several conditions such as bradycardia, tachycardia, myocardial intimation and bundle branch block can be diagnosed from the QRS complex alone. As a result, some embodiments in accordance with the present disclosure may collect and analyze only the QRS complex of a set of ECG beats, to the exclusion of the other waves. In other embodiments, one or more of the P, Q, R, S, T, and U waves may be analyzed individually or collectively in order to obtain more complete diagnostic or condition-focused information.
The embodiments of the present disclosure may use a generative ECG model configured to produce synthetic ECG signals based on a set of input parameters. Some embodiments may use one or a combination of known dynamic model generators, such as ECGSYN. ECGSYN models an ECG signal as a point moving around a unit circle, and uses differential equations to describe its motion. The individual waves are modeled as Gaussian attractors/repellers placed at specific points on the circle. The inter-beat features of ECG are modeled using 3 parameters: hrmean, hrstd and lfhfratio, corresponding to mean heart rate, standard deviation of heart rate and LF/HF ratio respectively. For the morphology features, each wave is represented by 3 parameters: (a, b, θ), which determine its height, width and distance to R peak, respectively. For example, the Q wave is represented by the 3-tuple (aQ, bQ, and θQ).
Referring to
The sensor platform 22 may be configured to receive data from the sensors 20 and to communicate data to a gateway device 24. In some embodiments, the sensor platform 22 may be a standalone device that transmits data through wired or wireless electrical connection to the gateway device 24. An example of such a sensor platform 22 is the SHIMMER Wireless Sensor Unit/Platform SH-SHIM-KIT-004, which is configured to transmit data via Bluetooth to a Bluetooth-enabled gateway device 24. In other embodiments, the sensor platform 22 may be a hardware or software module attached to or contained within the gateway device 24. The sensor platform 22 may comprise computing hardware and software, including a CPU, memory, data storage, and input/output terminals, having sufficient computing capacity to implement the sensor platform module described below. The sensor platform 22 may therefore receive raw or processed sensor data from the sensors and perform additional processing on the sensor data before transmitting data to the gateway device 24. In some embodiments, such as those embodiments implementing the methods described in detail below, the sensor platform 22 may receive a sensed ECG signal from the sensors, process the sensed ECG signal to produce deviation data, and transmit the deviation data to the gateway device 24.
The gateway device 24 may be configured to receive data from the sensor platform 22 and to communicate the data to a base station 26. The gateway device 24 may be any device suitable for receiving data transmitted by the sensor platform 22, which may be over a first communication network, and for transmitting the data to the base station 26 over a second communication network, which may be different from or may use different communication protocols or data security measures than the first communication network. In some embodiments, the gateway device 24 may be a personal mobile communication device, such as a smartphone. The gateway device 24 may communicate with the sensor platform 22 via Bluetooth, wired or wireless Local Area Network, or another limited-range wireless communication protocol or network. The gateway device 24 may communicate with the base station 26, which may be remote from the gateway device 24, via a cell network, a Wide Area Network, a telephone network, or another long-range data transfer network. The described communication system may use any suitable data encryption algorithm, username/password authentication, and other forms of data security to protect transmitted data.
In some embodiments, the base station 26 may be a computer, such as a personal computer, medical office or hospital server or mainframe, or another suitable computer for receiving data from the gateway device 24 and processing the data in order to display ECG information to a user, such as the patient or the patient's physician. The base station 26 may comprise computing hardware and software, including a CPU, memory, data storage, and input/output terminals, having sufficient computing capacity to implement the base station module described below. The base station 26 may therefore be configured to communicate with a body sensor network in order to receive a training ECG signal and to generate, distribute, and use a model ECG signal according to the present disclosure. In some embodiments, the base station 26 may be sufficiently robust to operate the ECGSYN dynamic model generator or another similar model generator.
Other embodiments in accordance with the invention may omit the gateway device 24. In such an embodiment, the sensor platform 22 may communicate the sensor data directly to the base station 26. The sensor platform 22 may be a standalone device as described above, or may be a hardware or software module attached to or contained within the base station 26. The base station 26 may be a personal mobile device such as a smartphone configured with Bluetooth or other data sharing technology, and further having a user interface for presenting ECG and receiving user input.
Referring to
The base station module 32 may be configured to train a dynamic model ECG based on input from one or more training sensors 60 attached to the patient. Prior to deploying the BSN for the patient, the base station module 32 may receive training data comprising an ECG signal recorded by the training sensors 60. At node 52, model learning takes place, wherein the base station module 32 may execute a stored dynamic model generator, such as ECGSYN. The model generator takes the training data as input parameters to generate the model ECG. The base station module 32 may be configured to distribute the model ECG to any device in the BSN that uses the model ECG for processing.
During regular operation, the sensor platform module 30 may use the sensor platform instance 34 to, at node 40, generate a model ECG signal. The sensor platform module 30 may, at node 42, receive a sensed ECG signal from the sensors 20. Where the sensed ECG signal is a raw signal, the sensor platform module 30 may deliver the sensed ECG signal to a pre-processing module 36 that may be configured to format the sensed ECG signal for comparison to the model ECG signal as described in detail below. At nodes 44 and 48, the sensor platform module 30 may compare the sensed ECG signal to the model ECG signal. Specifically, at node 44 the sensor platform module 30 may compare the morphology features of the two ECG signals, and at node 48 the sensor platform module 30 may compare the interbeat features of the two ECG signals. If the ECG signals match within one or more predefined thresholds, the sensor platform module 30 may not report any data to the base station module 32. Conversely, if the sensed data deviates from the model beyond the thresholds, the sensor platform module 30 may transmit one or more data updates to the base station module 32. Specifically, at node 46 the sensor platform module 30 may transmit one or more deviation values, and at node 50 the sensor platform module 30 may transmit a portion of the sensed ECG signal as raw data. These comparisons and transmissions are described in detail below.
Returning to the base station module 32, at node 58 the base station module 32 may use the base station instance 38 of the ECG model to generate an output ECG and transmit the output ECG to a display device 62. The base station instance of the ECG model may be updated at node 54 using received deviation values as input parameters to update the corresponding parameters of the ECG model. Node 58 may further include temporally aligning the ECG model with the sensed ECG signal received at node 56 as raw data. Thus, while no data is received from the sensor platform module 30, the base station module 30 assumes that the ECG of the patient is close to the ECG model and uses the model to generate a synthetic ECG signal, which is used at the display device 62 to represent the patient's ECG. When data is received from the sensor platform module 30, it may be directly recorded as the patient's ECG to modify the representation of the patient's ECG at the display device 62. The sensor platform module 30 may be configured to periodically transmit connection acknowledgement messages to the base station module 32 so that the base station module 32 may differentiate between periods of conforming ECG signal (i.e. no data sent) and device or network failure.
Several features of ECG data, such as mean heart rate and the LF/HF ratio, vary over time with activities such as sleeping, walking and exercise. As a result, a single, static ECG model may not effectively represent a patient's ECG over extended periods of time. For effective operation, the present BSN may dynamically update the ECG model as the patient's ECG changes. Since the deviation of sensed ECG from model-based values is first detected at the sensor platform module 30, the sensor platform module 30 may trigger the modifications to the ECG model through communication of data to the base station module 30. This may be achieved on the computationally-limited sensor platform 22 using one or a combination of feature updates and raw signal updates. For feature updates, interbeat features of the sensed ECG signal (e.g. mean heart rate) may be calculated from sensed data, and when these values change significantly, the sensor platform module 30 may update the corresponding parameters of its own instance 34 of the ECG model, and further may report the calculated deviation values to the base station module 32 for updating the base station instance 38 as described above. For raw signal updates, when the morphology of the patient's ECG deviates from the ECG model, the sensor platform module 30 may send the raw sensed data to the base station module 32. Based on received data, the base station module 32 may derive new parameter values for the ECG model using the model learning functionality at node 52. These values may be communicated to the sensor platform module 30 for updating the sensor platform instance 34.
The deviations may be reported first to the gateway device 24, which stores the deviations at step 86. The present method provides reduced ECG data size for storage by representing ECG using model parameters instead of data samples. For example, for a time interval denoted [tA, tB], if the patient's ECG follows the ECG model with parameter values [p1, p2, . . . PN], the data can be stored in a table or database as: “[tA, tB]:[p1, p2, . . . PN]”. These values can be used at a later time as inputs to the ECG model to regenerate the corresponding ECG data. This representation significantly reduces data size, and can enable local storage of ECG data on a resource-limited device, such as the patient's smartphone, which is not feasible with direct storage of sample values. The deviation data may also or alternatively be stored on the sensor platform 22 or base station 26. At step 88, the base station module 32 may receive the deviation data and use the deviation data as input parameters to update the ECG model at step 90. At step 92, the base station module may temporally align any abnormal ECG signal with the model ECG signal and create a modified output ECG signal for displaying the abnormal ECG.
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At step 104, the morphology parameters may be calculated from the patient's ECG. These parameters may include the (a, b, θ) parameters for each of the P, Q, R, S, T, and U waves. In one embodiment where only the QRS complex is evaluated, to the exclusion of the other waves, only 9 paratneters (aQ, aR, aS, bQ, bR, bS, θQ, θR, θS) are used to represent the beat morphology. Referring to
Thus, the interbeat and morphology parameters are learned from the patient's ECG and used to generate a matching synthetic ECG. The morphology of ECG may depend on the lead configuration of the sensors, and may vary across patients. Hence, the data used for learning the model should be obtained from the intended user of the system, and using the same lead configurations for training sensors 60 that are used for sensors 20. Referring back to
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1) Scaling (step 140): the amplitude of the sensed ECG signal is highly dependent on the sensor 20 hardware and the ECG lead configuration of the sensor 20. To ensure an accurate comparison between the sensed ECG signal and the model ECG signal, both signals may be converted to a normalized, device-independent scale. This is achieved by linearly scaling each signal to a maximum of 1.2 mV and minimum of −0.4 mV.
2) Filtering (step 142): the sensed ECG signal is typically noisy, and may be filtered to remove the noise. For extracting the QRS complex, a passband of 5-12 Hz may be achieved by cascading lowpass and highpass filters with cutoff frequencies at about 5 Hz and about 12 Hz, respectively. For low computational overhead, a Finite Impulse Response (FIR) filter design of 6 taps and order 32 may be used.
3) Peak Detection (step 144): measuring ECG features such as R-R intervals or QRS complex width requires the identification of Q, R, and S peaks.
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It will be appreciated by those skilled in the art that while the invention has been described above in connection with particular embodiments and examples, the invention is not necessarily so limited, and that numerous other embodiments, examples, uses, modifications and departures from the embodiments, examples and uses are intended to be encompassed by the claims attached hereto. The entire disclosure of each patent and publication cited herein is incorporated by reference, as if each such patent or publication were individually incorporated by reference herein. Various features and advantages of the invention are set forth in the following claims.
This Application claims priority to U.S. Provisional Patent Application Ser. No. 61/650,560 filed May 23, 2012, incorporated by reference herein in its entirety.
Research described in this application was partially funded by ARO MURI Grant Number W911NF0710287 and NSF grant CT-0831544. The government has certain rights in this invention.
Number | Date | Country | |
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61650560 | May 2012 | US |