AliveCor focuses on the creation of machine learning techniques to enable proactive heart care. It offers artificial intelligence (AI) enabled, machine learning (ML) powered electrocardiogram (ECG) sensors that deliver heart data at any time. The company's digital tools help patients access, manage and share their data, and connect with cardiologists to understand and manage their heart health. It was formerly known as AliveUSA. AliveCor was founded in 2011 and is based in Mountain View, California.
ESPs containing AliveCor
The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.
The in-patient hospital monitoring market includes a range of medical devices and technologies designed to track and analyze patient data in real-time in the hospital setting. They offer continuous monitoring of vital signs such as heart rate, blood pressure, oxygen levels, and more. This data helps healthcare professionals make informed decisions, identify early warning signs of deterioration, an…
Research containing AliveCor
Get data-driven expert analysis from the CB Insights Intelligence Unit.
CB Insights Intelligence Analysts have mentioned AliveCor in 13 CB Insights research briefs, most recently on Aug 15, 2023.
Expert Collections containing AliveCor
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
AliveCor is included in 5 Expert Collections, including Tech IPO Pipeline.
Tech IPO Pipeline
Companies developing artificial intelligence solutions, including cross-industry applications, industry-specific products, and AI infrastructure solutions.
Value-Based Care & Population Health
The VBC & Population Health collection includes companies that enable and deliver care models that address the health needs for defining populations along the continuum of care, including in the community setting, through participation, engagement, and targeted interventions.
The digital health collection includes vendors developing software, platforms, sensor & robotic hardware, health data infrastructure, and tech-enabled services in healthcare. The list excludes pureplay pharma/biopharma, sequencing instruments, gene editing, and assistive tech.
Companies developing, offering, or using electronic and telecommunication technologies to facilitate the delivery of health & wellness services from a distance. *Columns updated as regularly as possible; priority given to companies with the most and/or most recent funding.
AliveCor has filed 80 patents.
Liberal democracies, Cardiac electrophysiology, Republics, Smartwatches, Health informatics
Liberal democracies, Cardiac electrophysiology, Republics, Smartwatches, Health informatics
Latest AliveCor News
Sep 18, 2023
Journal of Medical Internet Research This paper is in the following e-collection/theme issue: January 16, 2023 . Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study Authors of this article: 2Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea 3Department of Internal Medicine, Sihwa Medical Center, Siheung-si, Republic of Korea Corresponding Author: 82, Gumi-Ro 173 Abstract Background: While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation. Objective: We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates. Methods: In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ≥19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ≥2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists. Results: Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ≥4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively. Conclusions: This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation. Trial Registration: ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268 J Med Internet Res 2023;25:e45760 Introduction Stroke prevention, which is frequently associated with atrial fibrillation, is currently a leading global health concern [ 1 ]. Importantly, atrial fibrillation increases the risk of stroke 5-fold and accounts for approximately 25% of cryptogenic strokes [ 2 ]. These events are largely preventable through the use of anticoagulant therapy, and the advent of nonvitamin K antagonist oral anticoagulants has resulted in better patient adherence and compliance in addition to better outcomes [ 3 , 4 ]. Despite early detection and accurate diagnosis of arrhythmias being crucial for preventing adverse outcomes [ 5 ], atrial fibrillation is often both asymptomatic and intermittent, making it difficult to capture these episodic events [ 6 , 7 ]. While conventional electrocardiogram (ECG) monitoring devices, including multilead portable ECG monitoring devices, event-detection monitoring devices, and implantable ECG monitoring devices, are useful for the early detection of atrial fibrillation, they also have considerable drawbacks including the requirement for multiple outpatient visits as well as invasive device implantation [ 8 ]. To overcome these disadvantages, several newer-generation ECG monitoring devices with advanced technologies have been developed [ 9 ]. Of these, Zio Patch (iRhythm Technologies) is a single-use patch-type ECG monitoring device that is capable of continuously monitoring a patient’s ECG signal for 2 weeks, as demonstrated through its application to more than 400,000 patients [ 10 ]. In comparison, AliveCor KardiaMobile (AliveCor Inc) is a smartphone-connected ECG monitoring device that can measure the single-lead ECG signal of a patient and following Food and Drug Administration clearance in 2014, has been widely used to detect symptomatic arrhythmias such as atrial fibrillation [ 11 ]. Unlike conventional ECG monitoring devices, these new-generation ECG monitoring devices allow for increased monitoring duration, wireless data transfer, and their portable size substantially decreases the impact on the daily life of patients [ 12 ]. In addition to these devices, the AT-Patch (ATsens) is a single-lead ECG monitoring device that is capable of continuously monitoring an ECG signal for up to 14 days and also enables the correlation of ECG changes and patient symptoms via a smartphone connection. Moreover, it has recently been demonstrated to have comparable diagnostic capability and safety to conventional ECG monitoring systems [ 13 ]. Despite this, the device is currently not widely used in real-world settings as it remains uncertain as to whether systematic screening for atrial fibrillation with cost- and labor-efficient patch devices, compared to traditional care, identifies older adults with previously undiagnosed atrial fibrillations more effectively or better improves their health outcomes. Furthermore, several studies have indicated high performance rates when using the validated congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score to predict new-onset atrial fibrillation, with over 10% of patients with CHA2DS2-VASc scores ≥2 having new-onset atrial fibrillations [ 14 ]. This study therefore sought to evaluate the validity of the CHA2DS2-VASc score for the early detection of new-onset atrial fibrillation in high-risk patients using the AT-Patch. Methods Ethics Approval This nonrandomized, noncontrol, multicenter, and prospective cohort study was carried out between November 2020 and April 2022 and was reviewed and approved by the institutional review board of the Seoul National University Bundang Hospital (B-2009-634-003). The protocol was registered with the Clinical Research Information Service of the Korea Centers for Disease Control and Prevention, Ministry of Health and Welfare, Republic of Korea (KCT0005650) on December 2, 2020. It was registered at ClinicalTrials.gov (NCT04857268) for international access to the protocol on April 23, 2021. Eligibility Criteria We enrolled 320 adults aged ≥19 years who visited 2 tertiary hospitals in Korea. Inclusion criteria were as follows: (1) patients who provided written and informed consent to participate and (2) those with CHA2DS2-VASc scores ≥2. The CHA2DS2-VASc score was calculated as follows: congestive heart failure, hypertension, diabetes mellitus, aged between 65 and 75 years, vascular disease, and female sex (1 point for each parameter), and stroke or transient ischemic attack and age >75 years (2 points for each). The maximum CHA2DS2-VASc score was 9 points. Congestive heart failure was defined as a previous diagnosis of heart failure (signs or symptoms of heart failure or objective evidence of reduced left ventricular ejection fraction ≤40%). Hypertension was defined as a systolic blood pressure greater than 140 mm Hg, diastolic blood pressure greater than 90 mm Hg, or prior antihypertensive drug use. Diabetes mellitus was defined as fasting glucose levels greater than 126 mg/dL or the presence of previous antidiabetic drug treatment. Vascular disease was defined as the prior occurrence of myocardial infarction, peripheral vascular disease, or aortic plaque [ 15 ]. The exclusion criteria were as follows: (1) previous diagnosis of atrial fibrillation; (2) implanted pacemaker, cardioverter-defibrillator, or any electrical device; (3) skin problems such as allergic contact dermatitis; and (4) female patients who were pregnant or lactating. AT-Patch Device The AT-Patch is a single-use device that can continuously record the electrical activity of the heart. This single-lead, noninvasive ECG recorder is indicated for ambulatory ECG monitoring for up to 14 days (11 days if the device is connected to a smartphone via Bluetooth). It weighs only 13 g, has a size of 95.0 × 50.6 × 8.3 mm, and allows for uninterrupted recording during sleep and light physical activity, thereby offering user comfort and increased patient adherence. A study coordinator placed this device on the patient’s left pectoral region, tilted 45° inward. To prevent noise or signal loss, the skin was cleansed using a 70% ethanol solution prior to attachment. Participants were instructed to wear the adhesive patch for as long as possible in order to obtain ECG data for up to 11 days. A continuous ECG signal was recorded for 11 days and stored on a memory card. Following this, the device was linked to a computer, and the data were subsequently downloaded and analyzed using AT-report, a specific program provided by ATsens Co, Ltd. Trial Schedule Clinical assessments were scheduled for baseline and days 11, 90, and 180. During the baseline assessment, we obtained the participants’ demographic data, past and present medical and drug administration history, and conducted physical examinations (height, body weight, vital signs such as systolic and diastolic blood pressure, and pulse), laboratory parameters, and 12-lead ECG results (if test results within the prior 4 weeks were available, they were used instead). For eligible participants, we attached the experimental AT-Patch device according to the institutional Good Clinical Practice for medical devices and recorded the date and time. During the following visit (day 11±5), we detached the device from the patient and analyzed the recorded data for atrial fibrillation detection. During the third visit (day 90±15), routine physical examinations (blood pressure and pulse) and 12-lead ECG were performed. Finally, during the last visit (day 180±15), the participants repeated the clinical assessments performed during the third visit. If atrial fibrillation or other types of arrhythmias were detected via either the AT-Patch or 12-lead ECG, the investigator scheduled the individual for further assessment. End Points The primary end point was the incidence of newly diagnosed atrial fibrillation as defined by ≥30 seconds of atrial fibrillation or flutter detected by the device. ECG data were reviewed by 2 independent cardiologists, and the diagnosis was confirmed when both reviewers were in agreement. The secondary end points were (1) a new clinical diagnosis of atrial fibrillation by 12-lead ECG at a scheduled or unscheduled follow-up visit and (2) death, acute myocardial infarction, stroke, or systemic embolic events during the 6-month monitoring period. Sample Size Calculation Previous studies reported the incidence of new-onset atrial fibrillation as 0.76 per 100 person-years in adults aged ≥50 years which gradually increased to 0.17-6.71, according to the CHA2DS2-VASc score [ 14 ] We hypothesized that the AT-Patch device would be capable of detecting 8% new-onset atrial fibrillation in high-risk patients with a CHA2DS2-VASc score ≥2. This is more than 8 times the expected incidence rate in these patients. We set the type I error to 0.05 and the confidence limit to 3% which resulted in 315 participants. Finally, we set the attrition rate to 1.5% which resulted in the final sample size of 320 participants. Statistical Analyses Data are presented as numbers and frequencies for categorical variables and as means and SDs for continuous variables. The incidence of newly diagnosed atrial fibrillation is described as the proportion and 95% CI. A 2-sided P value of <.05 was considered statistically significant. The Cohen κ coefficient was applied to evaluate interobserver reliability between the 2 independent cardiologists’ interpretations. McNemar’s test was applied to a 2 × 2 contingency table to determine the detection power of the AT-Patch device in comparison with the 12-lead ECG. For comparisons between patients with or without atrial fibrillation, the chi-square test (or Fisher’s exact test when any expected count was <5 for a 2 × 2 or 2 × 4 table) was performed for categorical variables, and a 2-sample t test or the Wilcoxon rank sum test was applied for continuous variables, dependent upon whether the data followed a normal distribution. Statistical analyses were performed using R software (version 3.1.0; R Foundation for Statistical Computing). Results Cardiac Arrhythmia Detection Rates A total of 320 participants were enrolled in this study. The incidence of arrhythmias, as detected by the AT-Patch and subsequently confirmed by 2 cardiologists (C1 and C2), is shown in Table 1 . Table 1. Incidence of arrhythmias detected using AT-Patch and confirmed by independent cardiologistsa. C1b, n (%) bC1: cardiologist 1. cC2: cardiologist 2. It was determined that atrial fibrillation occurred in 4.4% (14/320) and 3.8% (12/320) of patients by cardiologist 1 (C1) and cardiologist 2 (C2), respectively, with example recordings of a detected episode shown in Figure 1 . It is evident that this episode of atrial fibrillation began following a premature beat ( Figure 1 A) and thereafter presented as irregular heartbeats in the absence of P waves ( Figure 1 B). Moreover, the termination of atrial fibrillation as indicated by prolonged sinus pauses and sinus rhythm with clear P waves was captured by the patch device ( Figure 1 C). In total, 11 (3.4%) patients were concordantly diagnosed with atrial fibrillation by both cardiologists, while 3 arrhythmic events were discordantly diagnosed. In total, 1 cardiologist interpreted them as atrial fibrillations as they showed short episodes of irregular and uncertain P waves ( Figures 2 A-2C). However, the other cardiologist determined these episodes to be atrial tachycardia. Despite this, the overall Cohen κ coefficient was 0.840, which confirmed the almost perfect reliability of the 2 cardiologists’ interpretations. Detailed information on atrial fibrillation episodes detected by AT-Patch is presented in Table S1 in Multimedia Appendix 1 . Furthermore, 3 participants, in whom atrial fibrillation had not been detected by the patch device, were diagnosed with new-onset atrial fibrillation by the 12-lead conventional ECG at 3- or 6-month follow-up visits ( Table 2 ). Figure 1. Examples of (A) initiation of atrial fibrillation (arrow: a preceding premature beat), (B) sustainment, and (C) termination (arrow: appearance of the first P wave) as detected by the AT-Patch. Figure 2. Three representative examples of arrhythmic events that were differentially diagnosed as atrial fibrillation or atrial tachycardia by 2 cardiologists. Table 2. Detection of new-onset atrial fibrillation by AT-Patch versus conventional 12-lead ECGa. AT-Patch bThe 95% CI for the proportion of patients with AT-Patch (+) and (–) are shown in parentheses. Overall, the total number of patients with new-onset atrial fibrillation was 4.4% (14/320). Interestingly, none of the participants presented with atrial fibrillation with both the patch monitor and 12-lead ECG. Moreover, the cardiologists determined that nonsustained ventricular tachycardia occurred occasionally, in 4.1% (13/320) of patients, and supraventricular tachycardia occurred more frequently, in 46.6% (149/320) of patients ( Figure 3 ). No sustained ventricular tachycardia or fibrillation was detected using the patch device. Characteristics of the Participants Newly Diagnosed With Atrial Fibrillation Across the 320 participants with a CHA2DS2-VASc score ≥2, the mean age was 73.3 (SD 7.8) years, and the average CHA2DS2-VASc score was 3.6 (SD 1.2; Table 3 ). This study’s population consisted of 73.8% (236/320) of patients with hypertension, 40% (128/320) with diabetes mellitus, 6.2% (20/320) with previous heart failure, and 11.9% (38/320) with a history of stroke. In addition, 51.9% (166/320) of patients received β-blockers, and 65.9% (211/320) of patients received renin-angiotensin system inhibitors. Patients with new-onset atrial fibrillation detected by the AT-Patch alone had a history of heart failure significantly more often than those without (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). Interestingly, among the administered medications, the use of angiotensin-converting enzyme inhibitors was significantly different between patients with and without episodes of atrial fibrillation as detected by the AT-Patch device alone (n=4/11, 36.4% vs n=36/309, 11.7%, respectively; P=.04). When we evaluated patients with new-onset atrial fibrillation detected by AT-Patch or 12-lead ECG, those with previous heart failure were significantly different (n=6/14, 42.9% vs n=14/306, 4.6%; P<.001). Table 3. Profile of this study’s population and comparison between the atrial fibrillation group and the nonatrial fibrillation group. Characteristics aECG: electrocardiogram. According to the patch analysis system, normal sinus rhythm was detected in 98.2% of patients, and the average heart rate of this study’s population was 67.8 (SD 8.6) per minute. The participants were divided into atrial fibrillation and the nonatrial fibrillation groups as classified by AT-Patch only and by AT-Patch or 12-lead ECG. When comparing the recorded ECG data of the atrial fibrillation and the nonatrial fibrillation groups classified by AT-Patch only and by AT-Patch or 12-lead ECG, both classifications similarly showed that supraventricular (3.4% vs 0.4%; P=.001 and 4.6% vs 1.2%; P<.001, respectively) and ventricular (5.2% vs 1.2%, P<.001 and 2.8% vs 0.4%; P<.001, respectively) ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group. Clinical Outcomes and Adverse Events Few events occurred during the 6-month follow-up period, with no significant differences between the atrial fibrillation and nonatrial fibrillation groups ( Table 6 ). Table 6. Clinical outcomes and adverse events. Total, n (%)
AliveCor Frequently Asked Questions (FAQ)
When was AliveCor founded?
AliveCor was founded in 2011.
Where is AliveCor's headquarters?
AliveCor's headquarters is located at 444 Castro Street, Mountain View.
What is AliveCor's latest funding round?
AliveCor's latest funding round is Series F.
How much did AliveCor raise?
AliveCor raised a total of $134.33M.
Who are the investors of AliveCor?
Investors of AliveCor include Qualcomm Ventures, Khosla Ventures, BOLD Capital Partners, WP Global Partners, NGK Spark Plug and 9 more.
Who are AliveCor's competitors?
Competitors of AliveCor include Happitech, Healthy.io, Powerful Medical, Eko, Wellysis and 12 more.
Compare AliveCor to Competitors
Heart Care helps users have healthier routines and cultivates the habit of constant health monitoring. Heart Care is not a clinical tool, but a health and wellness application. The app is a simple and intuitive design, where users can easily find all the tools they need to increase health and well-being.
Happitech develops a heart rhythm software development kit for Android and iPhone operating systems. The company offers algorithms allowing medical-grade vital signs monitoring directly through a smartphone. The company was founded in 2015 and is based in Amsterdam, Netherlands.
Eko creates digital health solutions. Its solutions include artificial intelligence (AI) enabled stethoscopes, patient and provider software, and AI-powered analytics that enable doctors, nurses, researchers, and entire health systems to change care for the heart. It serves clients in the healthcare sector. It was founded in 2013 and is based in Oakland, California.
Sanolla provides artificial intelligence (AI) based primary care diagnostic solutions. It enables early detection and interpretation of asthma, heart failure, and chronic obstructive pulmonary disease (COPD) using infrasound. It was founded in 2016 and is based in Nesher, Israel.
Steth IO offers a telemedicine platform. It develops medical devices to increase the accuracy of essential medical practices. The company offers products that include a smartphone stethoscope for visualizing and hearing heart sounds. It was founded in 2015 and is based in Bothell, Washington.
FibriCheck offers a screening and monitoring application for irregular heart rhythms including atrial fibrillation. The company provides a mobile solution that measures the user's cardiac rhythm with a smartphone camera. It automatically shares the data with a healthcare professional to streamline diagnosis and treatment. The company was founded in 2014 and is based in Hasselt, Belgium.