Patent Application
20230165502
The present disclosure relates to medical devices, systems, and methods and in particular, to devices for providing electrocardiogram (ECG) monitoring.
Cardiovascular diseases are the leading cause of death in the world. In 2008, 30% of all global death were attributed to cardiovascular diseases. It is also estimated that by 2030, over 23 million people will die from cardiovascular diseases annually. Cardiovascular diseases are prevalent across populations of first and third world countries alike, and affect people regardless of socioeconomic status.
Arrhythmia is a cardiac condition in which the electrical activity of the heart is irregular or is faster (tachycardia) or slower (bradycardia) than normal. Although many arrhythmias are not life-threatening, some can cause cardiac arrest and even sudden cardiac death. Indeed, cardiac arrhythmias are one of the most common causes of death when travelling to a hospital. Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. In A-fib, electrical conduction through the ventricles of heart is irregular and disorganized. While A-fib may cause no symptoms, it is often associated with palpitations, shortness of breath, fainting, chest pain or congestive heart failure and also increases the risk of stroke. A-fib is usually diagnosed by taking an electrocardiogram (ECG) of a subject. To treat A-fib, a patient may take medications to slow heart rate or modify the rhythm of the heart. Patients may also take anticoagulants to prevent stroke or may even undergo surgical intervention including cardiac ablation to treat A-fib. In another example, an ECG may provide decision support for Acute Coronary Syndromes (ACS) by interpreting various rhythm and morphology conditions, including Myocardial Infarction (MI) and Ischemia.
Often, a patient with A-fib (or other type of arrhythmia) is monitored for extended periods of time to manage the disease. For example, a patient may be provided with a Holter monitor or other ambulatory electrocardiography device to continuously monitor the electrical activity of the cardiovascular system for e.g., at least 24 hours. Such monitoring can be critical in detecting conditions such as acute coronary syndrome (ACS), among others.
Prehospital ECG has been found to significantly reduce time-to-treatment for patients with possible ACS when symptoms present and shows better survival rates. The time-to-first-ECG is so vital that it is a quality and performance metric monitored by several regulatory bodies. According to the national health statistics for 2015, over 7 million people visited the emergency department (ED) in the United States (U.S.) with the primary complaint of chest pain or related symptoms of ACS. In the U.S., ED visits are increasing at a rate of or 3.2% annually and outside the U.S. ED visits are increasing at 3% to 7%, annually.
Embodiments and implementations of the present disclosure will be understood more fully from the detailed description given below and from the accompanying drawings of various aspects and implementations of the disclosure, which, however, should not be taken to limit the disclosure to the specific embodiments or implementations, but are for explanation and understanding only.
It is to be understood that the present disclosure is not limited in its application to the details of construction, experiments, exemplary data, and/or the arrangement of the components set forth in the following description. The embodiments of the present disclosure are capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the terminology employed herein is for purpose of description and should not be regarded as limiting.
In the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the concepts within the disclosure can be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
An electrocardiogram (ECG) provides a number of ECG waveforms that represent the electrical activity of a person's heart. An ECG monitoring device may comprise a set of electrodes for recording the ECG waveforms (also referred to herein as “taking an ECG”) of the patient's heart. The skin of the patient may come into contact with the set of electrodes at multiple locations, and the electrical signal recorded between each electrode pair in the set of electrodes may be referred to as a lead. Varying numbers of leads can be used take an ECG, and different numbers and combinations of electrodes can be used to form the various leads. Example numbers of leads used for taking ECGs are 1, 2, 6, and 12 leads.
The ECG waveforms (each one corresponding to a lead of the ECG) recorded by the ECG monitoring device may comprise data corresponding to the electrical activity of the person's heart. A typical heartbeat may include several variations of electrical potential, which may be classified into waves and complexes, including a P wave, a QRS complex, a T wave, and a U wave among others, as is known in the art. Stated differently, each ECG waveform may include a P wave, a QRS complex, a T wave, and a U wave among others, as is known in the art. The shape and duration of these waves may be related to various characteristics of the person's heart such as the size of the person's atrium (e.g., indicating atrial enlargement) and can be a first source of heartbeat characteristics unique to a person. The ECG waveforms may be analyzed (typically after standard filtering and “cleaning” of the signals) for various indicators that are useful in detecting cardiac events or status, such as cardiac arrhythmia detection and characterization. Such indicators may include ECG waveform amplitude and morphology (e.g., QRS complex amplitude and morphology), R wave-ST segment and T wave amplitude analysis, and heart rate variability (HRV), for example.
As noted above, ECG waveforms are generated from measuring multiple leads (each lead formed by a different electrode pair), and the ECG waveform obtained from each different electrode pair/lead may be different/unique (e.g., may have different morphologies/amplitudes). This is because although the various leads may analyze the same electrical events, each one may do so from a different angle.
There are different “standard” configurations for where electrodes may contact the patient. For example, an electrode in contact with the right arm can be referred to as RA. The electrode in contact with the left arm can be referred to as LA. The RA and LA electrodes may be in contact with the same location on the left and right arms, preferably near the wrist in some embodiments. The leg electrodes can be referred to as RL for the right leg and LL for the left leg. The RL and LL electrodes may be in contact with the same location for the left and right legs, preferably near the ankle in some embodiments. Lead I is typically the voltage between the left arm (LA) and right arm (RA), e.g. I=LA−RA. Lead II is typically the voltage between the left leg (LL) and right arm (RA), e.g. II=LL−RA. Lead III is the typically voltage between the left leg (LL) and left arm (LA), e.g. III=LL−LA. Augmented limb leads can also be determined from RA, RL, LL, and LA. The augmented vector right (aVR) lead is equal to RA−(LA+LL)/2 or −(I+II)/2. The augmented vector left (aVL) lead is equal to LA−(RA+LL)/2 or I−II/2. The augmented vector foot (aVF) lead is equal to LL−(RA+LA)/2 or II−I/2. Thus, a 6-lead ECG may be obtained under a standard electrode configuration by using three or more electrodes to measure three voltage differences (leads I, II, and II) and deriving three augmented vectors (aVR, aVL, and aVF) as further discussed in
It should be noted that a set of three or more leads may be transformed to generate a full, 12-lead ECG. Such transformation may be performed using a machine learning model (e.g., a neural network, deep-learning techniques, etc.). The machine learning model may be trained using 12-lead ECG data corresponding to a population of individuals. The data, before being input into the machine learning model, may be pre-processed to filter the data in a manner suitable for the application. For example, data may be categorized according to height, gender, weight, nationality, etc. before being used to train one or more machine learning models, such that the resulting one or models are finely-tuned the specific types of individuals. In a further embodiment, the machine learning model may be further trained based on a user's own ECG data, to fine-tune and personalize the model even further to decrease any residual synthesis error.
Lead I+Lead II/3.
As discussed herein, a 6-lead ECG may be acquired as early as possible for patients with possible ACS when symptoms present because prehospital ECG has been found to significantly reduce time-to-treatment and shows better survival rates. In addition, current ambulatory ECG devices such as Holter monitors, are typically bulky and difficult for subjects to administer without the aid of a medical professional. For example, the use of a Holter monitor requires a patient to wear a bulky device on their chest and precisely place a plurality of electrode leads on precise locations on their chest. These requirements can impede the activities of the subject, including their natural movement such as bathing and showering. Once an ECG is taken by such devices, the ECG is sent to the subject's physician who then analyzes the ECG waveforms and provides a diagnosis and other recommendations. Currently, this process is often performed through hospital administrators and health management organizations and many patients do not receive feedback in an expedient manner.
A number of handheld ECG measurement devices are known, including devices that may adapt existing mobile telecommunications devices (e.g., smartphones) so that they can be used to record an ECG. However, such devices either require the use of external (e.g., plug-in) electrodes, or include electrodes in a housing that are difficult to properly hold and apply to the body. Many ECG monitors are also limited to acquiring limb leads (e.g., due to size and other constraints). However, as people age, their QRS and T-wave vector may gradually move from the frontal plane to the horizontal plane, thus increasing the importance of acquiring data from a horizontal plane lead.
Embodiments of the present disclosure address the above and other problems by providing a 6-lead ECG monitoring device (hereinafter referred to as an ECG monitoring device) that may acquire 3 standard ECG leads and derive three augmented leads, while not requiring the use of adhesives for electrodes. The ECG monitoring device can be used by a user/patient, and provides ECG data to a user on a near instantaneous basis. For example, the ECG monitoring device may acquire leads I, II, and III and derive leads aVR, aVL, and aVF. However, any other combination of leads is possible. The ECG monitoring device may subsequently generate a 12-lead ECG using the three measured leads.
As discussed herein, for patients potentially suffering from ACS, including Myocardial Infarction (MI) and Ischemia, an ECG should be taken as early as possible to reduce the time to diagnosis and the time to treatment. The ECG monitoring device in accordance with embodiments of the present disclosure may provide decision support to physicians for ACS from the home of a patient itself, and provides a convenient way for doctors to order ECG tests and view reports as often as is necessary for them to manage the health of their patients, especially if doctors suspect ACS. In addition, the ECG monitoring device in accordance with embodiments of the present disclosure may prevent a patient from undergoing the inconvenience and disruption of an office visit and may save the cost and time of utilizing an ECG technician in the physician's office.
As shown in
In some embodiments, the band 205 may comprise a conductive fabric that enables the band 205 to function as one or more of the set of electrodes 210 (e.g., electrode 210C or 210D) or an additional electrode. In one example, the entire band 205 may be conductive on both the user facing side 203 and the non-user facing side 204 (e.g., may comprise conductive fabric to function as e.g., electrode 210A or electrodes 210C and 210D), which may allow one or more of the user facing side 203 and the non-user facing side 204 of the band 205 to act as an electrode. In these embodiments, the band 205 may comprise additional shielding to maintain adequate separation/isolation from any of the electrodes 210 (e.g., 210C and 210D) that are mounted on the band 205. In this way, contact with an electrode is always made while the ECG monitoring device 200 is being worn and the ECG monitoring device 200 may begin recording in response to detecting that any of the other electrodes 210 have been contacted.
In
The memory 407 may include a lead synthesis software module 407A (hereinafter referred to as module 407A) and an ECG waveform interpretation software module 407B (hereinafter referred to as module 407B). The processing device 406 may execute the module 407A to synthesize ECG waveforms corresponding to leads that were not measured by the electrodes of the ECG monitoring device 200 as discussed in further detail herein. The processing device 406 may execute the module 407B to generate diagnostic interpretations based on the measured and synthesized ECG waveforms, as discussed in further detail herein.
The housing 202 may further comprise a display unit 410 (e.g., an LCD touch screen or an AMOLED display to display ECG data among other information and notify the user when an ECG should or is being taken) and a transceiver 408, which may implement any appropriate protocol for transmitting ECG data wirelessly to one or more local and/or remote computing devices (not shown). For example, the transceiver 408 may comprise a Bluetooth™ chip for transmitting ECG data via Bluetooth to local computing devices (e.g., a laptop or smart phone of the user). In other embodiments, the transceiver 408 may include (or be coupled to) a network interface device (not shown) configured to connect with a cellular data network (e.g., using GSM, GSM plus EDGE, CDMA, quadband, or other cellular protocols) or a WiFi (e.g., an 802.11 protocol) network, in order to transmit the ECG data to a remote computing device (e.g., a computing device of a physician or healthcare provider) and/or a local computing device.
The ECG monitoring device 200 may take the 3-lead ECG in a standard configuration of the user (by utilizing the processing device 406 to measure the signal generated by the electrodes 210 and simultaneously record leads I, II, and III). The processing device 406 may subsequently derive any number of additional leads by executing module 407A as discussed hereinabove. For example, execution of the module 407A may cause the processing device 406 to synthesize the aVR, aVL, and aVF leads and then the V1, V2, V3, V4, V5, and V6 leads based on the I, II, III, aVR, aVL, and aVF leads using a lead conversion ML model (e.g., a state space model transform or neural network) to reconstruct a standard 12-lead ECG. By having the user constantly maintaining contact with electrode 210A, the processing device 406 may time align recordings when other electrodes come into contact with the user. In some embodiments, the processing device 406 (or the memory 407) may include firmware/logic to automatically derive the aVR, aVL, and aVF leads upon recording leads I, II, and III and may optionally execute module 407A to synthesize any appropriate number of the V leads.
The processing device 406 may then execute the module 207B in order to analyze the full 12-lead ECG waveform set and generate one or more interpretations (also referred to herein as diagnoses) based thereon using an interpretation ML model. The interpretation ML model may be based on any appropriate algorithm, for example GE™'s EK12 algorithms. The processing device 406 may detect (and generate interpretations indicating) conditions such as myocardial ischemia (anterior or lateral ischemia), MI (anterior or lateral MI), left and right bundle branch block, and right/left ventricular hypertrophy, among others.
The ECG monitoring device 200 may take the 3-lead ECG in a standard configuration of the user (by utilizing the processing device 406 to measure the signal generated by the electrodes 210 and band 205 while simultaneously recording leads I, II, and III). The processing device 406 may subsequently derive any number of additional leads by executing module 407A as discussed hereinabove. For example, execution of the module 407A may cause the processing device 406 to synthesize the aVR, aVL, and aVF leads and then the V1, V2, V3, V4, V5, and V6 leads based on the I, II, III, aVR, aVL, and aVF leads using a lead conversion ML model (e.g., a state space model transform or neural network) to reconstruct a standard 12-lead ECG. By having the user constantly maintaining contact with electrode 210A, the processing device 406 may time align recordings when other electrodes come into contact with the user. In some embodiments, the processing device 406 (or the memory 407) may include firmware/logic to automatically derive the aVR, aVL, and aVF leads upon recording leads I, II, and III and may optionally execute module 407A to synthesize any appropriate number of the V leads.
The processing device 406 may then execute the module 207B in order to analyze the full 12-lead ECG waveform set and generate one or more interpretations (also referred to herein as diagnoses) based thereon using an interpretation ML model. The interpretation ML model may be based on any appropriate algorithm, for example GE's EK12 algorithms. The processing device 406 may detect (and generate interpretations indicating) conditions such as myocardial ischemia (anterior or lateral ischemia), MI (anterior or lateral MI), left and right bundle branch block, and right/left ventricular hypertrophy, among others.
The ECG monitoring device 200 may take a 3-lead ECG in a non-standard configuration of the user (by utilizing the processing device 406 to measure the signal generated by the electrodes 210 and simultaneously record leads I, II, V2). The processing device 406 may subsequently derive any number of additional leads by executing module 407A as discussed hereinabove. For example, execution of the module 407A may cause the processing device 406 to synthesize the aVR, aVL, and aVF leads and then the V1, V2, V3, V4, V5, and V6 leads based on the I, II, III, aVR, aVL, and aVF leads using a lead conversion ML model (e.g., a state space model transform or neural network) to reconstruct a standard 12-lead ECG. By having the user constantly maintaining contact with electrode 210A, the processing device 406 may time align recordings when other electrodes come into contact with the user. In some embodiments, the processing device 406 (or the memory 407) may include firmware/logic to automatically derive the aVR, aVL, and aVF leads upon recording leads I, II, and III and may optionally execute module 407A to synthesize any appropriate number of the V leads.
The processing device 406 may then execute the module 207B in order to analyze the full 12-lead ECG waveform set and generate one or more interpretations (also referred to herein as diagnoses) based thereon using an interpretation ML model. The interpretation ML model may be based on any appropriate algorithm, for example GE's EK12 algorithms. The processing device 406 may detect (and generate interpretations indicating) conditions such as myocardial ischemia (anterior or lateral ischemia), MI (anterior or lateral MI), left and right bundle branch block, and right/left ventricular hypertrophy, among others.
At block 705, a first electrode of a plurality of electrodes of the ECG monitoring device 200 may maintain constant contract with a body of a user. As an example, the electrode 210A may maintain constant contact with either a left wrist (LA) or a right wrist (RA) of the user.
At block 710, a second and a third electrode of the plurality of electrodes of the ECG monitoring device 200 or a second, a third, and a fourth electrode of the plurality of electrodes of the ECG monitoring device 200 contact the body of the user. For examples, refer to
At block 715, processing device 406 of the ECG monitoring device 200 may determine that each of the plurality of electrodes is isolated from a location on the body of the user that another electrode of the plurality of electrodes is contacting.
At block 720, processing device 406 of the ECG monitoring device 200 may perform an ECG of the user by measuring the signal generated by the electrodes 210 and simultaneously recording leads I, II, and III. Processing device 406 may subsequently derive leads aVR, aVL, aVF, by executing module 407A as discussed hereinabove. The augmented vector right (aVR) lead is equal to RA−(LA+LL)/2 or −(I+II)/2, the augmented vector left (aVL) lead is equal to LA−(RA+LL)/2 or I−II/2, and the augmented vector foot (aVF) lead is equal to LL−(RA+LA)/2 or II−I/2. Processing device 406 may then also execute module 407A to synthesize the V1, V2, V3, V4, V5, and V6 leads based on the I, II, III, aVR, aVL, and aVF leads using a lead conversion ML model (e.g., a state space model transform or neural network) to reconstruct a standard 12-lead ECG. The processing device 406 may then execute the module 207B in order to analyze the full 12-lead ECG waveform set and generate one or more interpretations (also referred to herein as diagnoses) based thereon using an interpretation ML model. The interpretation ML model may be based on any appropriate algorithm, for example GE's EK12 algorithms. The processing device 406 may detect (and generate interpretations indicating) conditions such as myocardial ischemia (anterior or lateral ischemia), MI (anterior or lateral MI), left and right bundle branch block, and right/left ventricular hypertrophy, among others. ECG monitoring device 200 may then determine one or more diagnoses based on a set of ECG waveforms generated by the ECG and transmit, via transceiver 408, the one or more diagnoses to a computing device for the user or doctor's review.
In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, a hub, an access point, a network access control device, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. In one embodiment, computer system 800 may be representative of a server.
The exemplary computer system 800 includes a processing device 802, a main memory 804 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), a static memory 806 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 818, which communicate with each other via a bus 830. Any of the signals provided over various buses described herein may be time multiplexed with other signals and provided over one or more common buses. Additionally, the interconnection between circuit components or blocks may be shown as buses or as single signal lines. Each of the buses may alternatively be one or more single signal lines and each of the single signal lines may alternatively be buses.
Computing device 800 may further include a network interface device 808 which may communicate with a network 820. The computing device 800 also may include a video display unit 810 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse) and a signal generation device 815 (e.g., a speaker). In one embodiment, video display unit 810, alphanumeric input device 812, and cursor control device 814 may be combined into a single component or device (e.g., an LCD touch screen).
Processing device 802 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device may be complex instruction set computing (CISC) microprocessor, reduced instruction set computer (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processing device 802 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 802 is configured to execute 6-lead ECG instructions 822, for performing the operations and steps discussed herein.
The data storage device 818 may include a machine-readable storage medium 828, on which is stored one or more sets of 6-lead ECG instructions 822 (e.g., software) embodying any one or more of the methodologies of functions described herein. The 6-lead ECG instructions 822 may also reside, completely or at least partially, within the main memory 804 or within the processing device 802 during execution thereof by the computer system 800; the main memory 804 and the processing device 802 also constituting machine-readable storage media. The 6-lead ECG instructions 822 may further be transmitted or received over a network 820 via the network interface device 808.
While the machine-readable storage medium 828 is shown in an exemplary embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more sets of instructions. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read-only memory (ROM); random-access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or another type of medium suitable for storing electronic instructions.
The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth, in order to provide a good understanding of several embodiments of the present disclosure. It will be apparent to one skilled in the art, however, that at least some embodiments of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram format in order to avoid unnecessarily obscuring the present disclosure. Thus, the specific details set forth are merely exemplary. Particular embodiments may vary from these exemplary details and still be contemplated to be within the scope of the present disclosure.
Additionally, some embodiments may be practiced in distributed computing environments where the machine-readable medium is stored on and or executed by more than one computer system. In addition, the information transferred between computer systems may either be pulled or pushed across the communication medium connecting the computer systems.
Embodiments of the claimed subject matter include, but are not limited to, various operations described herein. These operations may be performed by hardware components, software, firmware, or a combination thereof.
Although the operations of the methods herein are shown and described in a particular order, the order of the operations of each method may be altered so that certain operations may be performed in an inverse order or so that certain operation may be performed, at least in part, concurrently with other operations. In another embodiment, instructions or sub-operations of distinct operations may be in an intermittent or alternating manner.
The above description of illustrated implementations of the invention, including what is described in the Abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. While specific implementations of, and examples for, the invention are described herein for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. The words “example” or “exemplary” are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “example” or “exemplary” is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X includes A or B” is intended to mean any of the natural inclusive permutations. That is, if X includes A; X includes B; or X includes both A and B, then “X includes A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Moreover, use of the term “an embodiment” or “one embodiment” or “an implementation” or “one implementation” throughout is not intended to mean the same embodiment or implementation unless described as such. Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. as used herein are meant as labels to distinguish among different elements and may not necessarily have an ordinal meaning according to their numerical designation.
It will be appreciated that variants of the above-disclosed and other features and functions, or alternatives thereof, may be combined into may other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims. The claims may encompass embodiments in hardware, software, or a combination thereof.
