Comprehensive ECG Monitoring with AI Analysis.
24-Hour ECG Monitoring
Get AI-analyzed reports via PC & App
Real-Time ECG/EKG Tracking via App
Built-in screen & event marker
Mini 12-Lead Holter Monitor
24-Hour ECG/EKG Monitoring
AI-ECG Analysis via PC & App
Real-time ECG Tracking via App
Event Marker
The main purpose of a Holter monitor is to identify the causes of heart irregularities. For example, experiencing frequent abnormal heart rates or episodes of fainting may be indicative of underlying heart conditions.
The Lepod Holter Monitor is a highly compact dynamic electrocardiogram device. Unlike traditional bulky Holter monitors, you can continue your daily activities without worry when wearing the device. It conducts initial screening for heart issues through 24-hour ECG recording and AI analysis. The ECG reports and electrocardiogram can then be sent to cardiologists for further diagnosis.
Featuring a high-resolution OLED display, this Holter monitor provides a more intuitive and detailed monitoring of cardiac activity.
Users are required to upload the ECG data to the PC software or Mac App, and subsequently download the AI-analyzed report along with the ECG/EKG waveform.
This AI-ECG Analysis System is able to identify up to 17 kinds of ECG/EKG events as below:
- Sinus Rhythm
- Ectopic Rhythm
- Sinus Tachycardia
- Sinus Bradycardia
- PAC(Premature Supraventricular Contraction)
- PVC(Premature Ventricular Contraction)
- Couplet of PAC
- Couplet of PVC
- PAC Bigeminy
- PVC Bigeminy
- PAC Trigeminy
- PVC Trigeminy
- Supraventricular Tachycardia
- Ventricular Tachycardia
- Atrial Flutter
- Atrial Fibrillation
- HRV
Reports can be saved as PDF files and sent to healthcare professionals for a more comprehensive understanding of your heart health.
Users can obtain detailed ECG/EKG reports and electrocardiograms through the AI-ECG analysis system, allowing them to gain further insight into their cardiac health status.
If the user senses a heart abnormality during the measurement, they can double-click the power button to mark the event. After exporting the ECG data to the app, access the record and click "View full ECG", you can then navigate to the corresponding marked events.
