Efficiently achieve IEC 60601 certification for your patient care applications
Why choose TI for your hospital patient care design?
Meet your safety requirements
Access our comprehensive product portfolio supporting the IEC 60601-1-8 standard for generating medical alarms in patient monitors.
Achieve reliable system operation
Obtain high working voltages and high reliability in your designs to enable extended equipment lifetimes and protect monitoring data.
Scale your biosensing analog front-end needs
Design entire stand-alone and multimodal analog signal chains for ECGs, PPGs, EEGs and ICGs.
Enhance battery protection
Enable multibattery backups and reduce solution size using our power-dense battery-management integrated circuits, which support any input source or charging topology.
Engineering advanced hospital patient care designs
Achieve IEC 60601-1-8 safety compliance in patient monitors
A patient’s life often depends on the proper functionality of medical devices, which must comply with basic safety and performance requirements defined by standards bodies such as the International Electrotechnical Commission (IEC). In most cases, the essential performance of a medical device includes raising both visual and auditory alarms in order to take corrective action.
Multiparameter patient monitors, neonatal warmers and incubators, anesthesia delivery systems, dialysis machines, infusion pumps, ventilators, and surgical equipment are all examples of medical equipment that need an alarm.
Demystifying medical alarm designs, part 1: IEC60601-1-8 standard requirements
Demystifying medical alarm design, part 2: Design inputs and existing techniques
IEC 60601-1-2-compliant isolation for reliable system operation
The IEC 60601-1-2 standard calls for the evaluation of critical distances in any medical equipment certification of digital isolators – specifically creepage, clearance and distance through insulation. Our products offer high working voltages and high reliability, enabling you to design equipment with extended lifetimes. Compliance with IEC 60601-1-2 also helps achieve safety in terms of data and power isolation.
Solving space challenges with isolated bias supplies
Integrated AFE ICs for PPG, ECG, EEG & ICG
Our biosensing analog front ends offer an entire stand-alone and multimodal analog signal chain for electrocardiograms (ECGs), photoplethysmographies (PPGs), electroencephalograms (EEGs), and indocyanine green angiographies (ICGs). For wearables and multimodal sensing needs, we have integrated all PPG, ECG and ICG signal chains into one device. We designed our analog front ends to address analog signal conditioning and analog-to-digital channel conversion, while enabling scalability and maximum flexibility in sensor and industrial designs.
No strings attached: Creating next-generation wireless patient monitors
Key considerations for designing electrocardiogram (ECG) front-end circuit
Manage multiple batteries and create scalable battery backup
Increase power density and overall battery and system protection in medical applications with our battery-management technology.
Benefits:
- Input and output over- and undervoltage and current protection in safety-critical applications.
- Support for multibattery chemistries (lithium ion, nickel metal hydride, lead acid, supercapacitor) and multicell configurations (one to six cells).
- Seamless transition to backup power in the event of a main power failure.
- Multibattery-pack solutions for flexible and scalable battery-backup solutions, with options for load sharing and adjustable output voltages.
Battery management deep dive on-demand technical training
A unified, open-source development platform lets you reuse code across projects
Get the most out of your designs with high-performance computer vision, sensor fusion and artificial intelligence (AI) processing, with easily programmable hardware accelerators across our processor families. The TI edge AI ecosystem gets you to market faster with production-ready solutions and online access to industry experts, whether you're designing with Python, TensorFlow Lite, Open Neural Network Exchange Runtime, Tensor Virtual Machine, GStreamer, Docker or Robot Operating System.