In this episode, we explore cutting-edge research aimed at tackling one of the leading causes of firefighter line-of-duty deaths: sudden cardiac events. Host [Your Name] speaks with Dr. Andy Tam (NIST) and Dr. Dillon Dzikowicz (University of Rochester) about their groundbreaking project combining AI-driven ECG analysis with wearable technology. Their goal? A real-time, portable monitoring system that can detect dangerous heart rhythms in firefighters before it’s too late.The conversation covers the science behind ischemic heart events, the challenges of collecting high-quality ECG data during firefighting, the role of machine learning in interpreting those signals, and the path from public research to a usable, life-saving product. You’ll also hear some lighter moments, including a debate about aliens and the quirks of wearable devices for tattooed users.

CONTACT DILLION:

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0:00 – 3:50 | Introduction & Guest BackgroundsHost introduces the episode’s focus: AI detecting abnormal heart rhythms in firefighters.Meet Dr. Andy Tam (mechanical engineering, machine learning, firefighting technology)Meet Dr. Dillion Dzikowicz (registered nurse, PhD, cardiovascular research in firefighters)3:51 – 4:13 | The “Wheel of Stupid Questions” IntroAcknowledging the show’s tradition of opening with fun, offbeat questions.4:24 – 8:02 | Stupid Question: Do You Believe in Aliens?Andy: Yes, as a mix of curiosity and belief.Dillion: No — prefers evidence-based conclusions.8:02 – 11:05 | The Problem: Sudden Cardiac Death in Firefighters100+ firefighter deaths annually in the U.S. from cardiac eventsPast interventions: diet, exercise, rehab — but missing the unique on-duty risk windowShift toward real-time monitoring during actual firefighting11:06 – 15:13 | Pathophysiology & Detection GoalsIschemic-induced arrhythmias as primary targetST segment changes as a key indicatorPredictive potential beyond real-time alerts15:13 – 18:49 | Machine Learning 101 for ECG InterpretationTraining AI to “think” like a cardiologistFiltering noise from movement artifactsImportance of firefighter-specific datasets18:50 – 21:49 | Wearable Device DevelopmentMoving from bulky Holter monitors to modern wearablesChoosing chest-strap placement over wrist devices for reliabilityFDA-cleared continuous ECG with ischemia-specific lead21:50 – 22:50 | Wearables & TattoosUnique challenges in signal detection through tattooed skinClinical validation study includes tattooed subjects22:51 – 27:01 | Software + Hardware CollaborationBalancing AI development with firefighter comfort & usabilityOpen questions about when/where to wear devices (on shift vs. during calls)Volunteer vs. career firefighter considerations27:02 – 32:32 | Data Collection & ValidationCurrent study: monitors worn during structural fire trainingAvoiding alarm fatigue with careful algorithm tuningCombining hospital abnormal-event data with real-world firefighter data32:33 – 39:20 | Model Performance & Future ApplicationsAccuracy: 95% with Holter data, 92% with wearable dataPotential expansion to police, military, EMSGoal: device-agnostic algorithms for broad accessibility39:20 – 45:05 | From Research to ProductRegulatory hurdles: FDA approval for “software as a medical device”Public funding and the bridge between science and businessFocus remains on saving lives over commercialization45:06 – 46:07 | Call for ParticipantsRecruiting volunteer, wildland, and career firefighters (18+) for ongoing studiesContact details provided in episode description and social media posts