ARGON MEDICAL DEVICES, INC. 049650163B Cardiac Quad Kit w/ 3 Transducers

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.

Zoll M series Biphasic 200 Joules Max Defibrillator. Battery and Cables Included.

First Aid Kit Wihout Drug (Content : Adhesive bandages in a variety of different sizes and shapes + Non-adherent sterile Gauze Pad + Cleansing wipe (with alcohol)) This type of convenience kit, as listed in the guidance above, is under enforcement discretion, and does not require a premarket notification (510(k)) to market if it meets all criteria in the guidance.

The Test of Variables of Attention (T.O.V.A.) kit contains the hardware and software necessary to install and administer the T.O.V.A. continuous performance test. The T.O.V.A. provides healthcare professionals with objective measurements of attention and inhibitory control. The visual T.O.V.A. aids in the assessment of, and evaluation of treatment for, attention deficits, including attention-deficit/hyperactivity disorder (ADHD). The auditory T.O.V.A. aids in the assessment of attention deficits, including ADHD. T.O.V.A. results should only be interpreted by qualified professionals.

KIT, SMALL POSITIONING, Set of 7

TIDI Positioning Aid Kits Kit, Various Sizes 7 per Set

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.