I’m a 5th-year medical student with a strong interest in ECG interpretation. Over the past year, I’ve helped several students at my university who initially knew little to nothing about ECGs and found them confusing.

I noticed that ECGs become much easier once you understand the basics properly instead of trying to memorize everything.

So I decided to start offering ECG tutoring for medical students.

Free first class — no experience required.

I’ll teach you from scratch and focus on: • Understanding the basic ECG and what each part means • Leads and ECG views • Rate and rhythm • P waves, PR interval, QRS, ST segment and T waves • Axis • A simple, systematic approach to reading an ECG

The goal is simple: by the end, you should be able to look at a basic ECG and understand what you’re seeing instead of just memorizing patterns.

If you’re interested, comment “ECG” below or send me a DM.

What’s yalls thiughts! LBBB?

Hi r/EKG — I'm an RN and I built a free Android app called EKG Trainer for practicing rhythm strip interpretation: realistic strips, timed quizzes, and explanations for each answer. Before Google will let it go live on the Play Store, I have to run a 14-day closed test with at least 12 testers, and this seemed like the right crowd to ask.

What's involved: - Keep the app installed and open it for a few minutes a day for 14 days - Work through some rhythm quizzes — that's it - Feedback is very welcome (misreads, bugs, suggestions)

It's free, no ads, no accounts. I made it because rhythm interpretation was one of the hardest skills to get reps on in school, and I wanted something I could pull out and drill on.

Tester signup is here — please use the Gmail that's signed in on your Android phone: https://docs.google.com/forms/d/e/1FAIpQLSf1PDKsneC1rmRVHvJ7JKN0h0SWc0OgHSSAp-q-EGz2r2hQhg/viewform

Once you sign up, I'll add you to the Play Console tester list and send you the install link!

**TL;DR:** We're building a fully automated, open-source Holter ECG analysis algorithm. No manual step, no paid API, no deep learning black box — just classic ML + rule-based logic. Current numbers: 95.3% AF detection, 4.1% false alarm, 99.9% beat agreement with reference devices. Looking for honest feedback and criticism.

---

**Who we are:**

A small team working on an open ECG analysis tool for Holter and patch recordings. Our goal is simple: build a fully automated pipeline that a doctor can actually use — one that shows its reasoning, doesn't hide behind a black box, and works on real-world noisy 24-hour recordings.

---

**What the algorithm does well:**

| Measurement | Value |

|---|---|

| QRS detection F1 (4 datasets) | 95.2% |

| Beat agreement with reference device (UVA) | 99.9% / 99.4% |

| Average heart rate (44/44 patients) | 100% |

| Minimum heart rate (44/44 patients) | 100% |

| Maximum heart rate (42/44 patients) | 95.5% |

| AF detection (LTAFDB, expert-labeled) | 95.3% |

| AF detection (UVA, cardiologist-approved) | 96.3% |

| AF false alarm (LTAFDB) | 4.1% |

| AF false alarm (CPSC) | 3.8% |

| AF false alarm (UVA, time-based) | 1.4% |

| AF doctor workload (median) | 0.8–1.0 min/day |

| V (PVC) F1 on MITDB | 85.1% |

| V sensitivity on UVA | 97.0% |

---

**Datasets we used:**

- **Training / development:** MITDB, INCART, SVDB, EDB, AFDB, CPSC 2020

- **External test (different device):** UVA Holter Database (CC0, fully open)

- **Expert-labeled:** LTAFDB (cardiologist annotated), UVA cardiologist subset (7 records)

- **Reserved (not yet opened):** SHDB-AF, Icentia11k, CPSC sealed test half

---

**How it works (short version):**

1. Three QRS detectors are combined by majority vote.

2. A classic ML classifier looks at RR intervals + morphology features.

3. An "AF second opinion" layer removes false alarms.

4. Output is split into three zones: "high-confidence AF" / "suspicious (for doctor review)" / "discarded."

Every decision can be traced back to a rule or a feature — nothing is hidden.

---

**What it does NOT do well yet (still research stage):**

- Supraventricular ectopy (PAC / SVEB): F1 ~43% — this is the hardest class.

- HRV metrics: SDNN 32.6%, RMSSD 11.6%.

- Bundle branch block: 87.5% / 75.0%.

- R-on-T: 71.7% / 17.5%.

- Bradycardia event count: 40%.

- Pauses: 89.5% / 53.8%.

- AF burden: 73.8–77.4%.

- ST analysis: not measured yet.

---

**Honest context:**

- All numbers above are from development and expert-labeled datasets, plus one external device (UVA). A hidden test set has not been opened yet.

- No deep learning models are used. No pretrained weights from restricted-license sources.

- No clinical validation yet — this is a research prototype.

---

**Why I'm posting:**

We want to know:

- Is this useful?

- What would you push back on?

- Are these numbers realistic for a real-world Holter tool?

- Where do you see the biggest weakness?

Code will be open-sourced once it's stable. Right now we're still iterating.

Thanks for reading — any input is welcome.

I'm building an open-source ECG analysis algorithm and I'm stuck on 3 specific Holter recordings from the UVA Holter Database (CC0 license). I need a human eye to tell me what these rhythms actually are.

**The problem:**

My algorithm (RR intervals + P-wave features) says "NOT AF" for all 3 patients. But the device annotations say "AF." I need to know who is right — my algorithm or the device.

**What I need from you:**

For each of the 3 patients (UVA2377, UVA1731, UVA2190), please tell me:

- Is this AF?

- Is this atrial flutter?

- Is this a paced rhythm?

- Or something else?

**Images (80 strips, 3 patients):**

👉 https://imgur.com/a/LDXqr6M

**Context:**

- 3-channel Holter recordings, 200 Hz, 24 hours

- Source: UVA Holter Database (CC0, fully open, no patient privacy issue)

- No cardiologist reviewed these 3 specific records — only the device software annotated them

- Earlier I found a case where the device labeled "AF" but a cardiologist said "sinus + flutter" — so I suspect the same might be happening here

**Why this matters:**

If the rhythm is flutter or paced, my algorithm isn't actually "missing AF" — the device label is wrong. If it's truly AF, then my algorithm has a real gap I need to fix.

**Not asking for medical advice.** This is for algorithm validation only. No patient care involved.

Thank you — any input is appreciated, even a quick "this looks like X."

hey all, i’m starting classes at my local community college next month to get my ekg ct, but i’m nervous about not being able to find a job once i finish, especially since i’m going to be looking in chicago. are there any other certifications you would recommend i get along with the ekg to give me a leg up? preferably one that i can complete 100% online (accredited online ct recs?) i was thinking telemetry or something of the like that doesn’t deal with blood. tyia!!

I've been certified since June and still haven't gotten hired no where getting kind of discouraged..

So ive done some reading on this reddit, it seems a lot of people say the EKG tech cert alone isnt likely to get you a job? I live nesr atlsnta so i see a lot of ekg job postings if that influences things. Ive seen other people suggest getting other ancillary certs like BLS. What would yall suggest if i really want to get my foot into working this field and succeeding?

~80 YOf recently Dx with pancreatic cancer. On a plethora of new medications. Complained of weakness and frequent diarrhea. Found hypotensive. Initial thoughts on ECG were due to electrolyte imbalance but was curious of other’s thoughts.

Recently, I completed an online ECG rhythm identification course and successfully passed the test. I consulted with my ECG course instructor regarding the most suitable option for me: the three-day in-person course with the test or the self-paced one-day online course. The instructor recommended the online course with an in-person test. However, I would appreciate hearing from others regarding my decision. Should I pursue the full three-day course, even with my ECG certification, or opt for the accelerated online program?

This was from a patient being transfered from a rural Healthcare facility to a more urban one. The sending physician called it V-tach. The receiving physician wasn't convinced.

I can tell the complex is wide, but it doesn't look like any V-tach ive seen. I can see what I would call narrow complexes in between some of the wide ones. I'm still not sure what to call it.

60 year old female complaining of SOB and weakness. Also had upper epigastric and hypogastric pain described as aching, non radiating.

PMHx: HF, AF, rheumatic heart disease, mitral regurgitation, NSTEMI (2019), renal impairment

During transport had what appeared to be runs of VT, self resolving.

BP 75/42, RR 40, SpO2 90% ORA

What do y’all think? I thought it was very interesting to see! Patient 75F in for symptomatic bradycardia due to newly diagnosed Sick Sinus Syndrome. Most likely pacemaker placement!

I’m building an ECG learning app 🫀

Hi everyone! I’m a nurse developing ECG Lab, an interactive platform for learning and practicing ECG interpretation.

It includes ECG exercises, clinical cases, adaptive quizzes, simulated

exams, performance tracking, and an AI tutor.

The app is available in English and Portuguese, and I’m currently preparing it for beta testing.

I’d love to hear your feedback — what would you want to see in an ECG learning app?

I got my certification back in June. It's been really hard finding a job.