The following content has been provided by the University of Erlangen-Nürnberg.

Welcome to today's lecture on biomedical signal analysis.

I'm really happy that so many of you found the classroom today because we're looking at interesting topics

and as I experienced myself, we are just coming from a weekend, so just two days lecturing is kind of

not the usual.

I also hope you enjoyed last week's lecture. I had hoped that I would make it, but my plane from taking me back from

Boston just was a little bit late, so I couldn't do the lecture myself.

But I think that Stefan and Felix are quite experienced lecturers by this point in time, and

I hope that they brought across the important topic of ECG signal analysis in an inspiring and interesting way.

Maybe a few words about why I was not present, because you are also going to evaluate how often the teacher was

present and not present in the lecture.

So last week I had a few meetings with colleagues at the MIT Media Lab, the Massachusetts Institute of Technology,

and Harvard Medical School, where we have a couple of cooperation projects.

Actually also, master students, two of them, one at each institution financed by the German Academic Exchange Service.

So really interesting and inspiring projects, and they are both, well, one is mainly about biomedical signal analysis,

and the other one is about variable systems and biomedical signal analysis.

So you could say that I got a little bit of more information about the content of this course in order to bring you even better lecture material.

And I actually also brought home something which I'm going to share with you in one of the upcoming lectures about analysis of biomedical signals.

So that was last week. We are also going to do a little recap about the content of last week.

Before I'm stepping into content, I want to finish the introduction for today.

And part of the introduction for today is I have a guest lecturer here, Maria.

Could you maybe stand up for a second?

Maria is a visitor from Serbia. She's also a biomedical signal analysis expert, working a lot with signal analysis and signal generation.

And you will see more of Maria in the weeks to come because she is also going to be involved in teaching of this class.

So hi, Maria. She'll join us for the rest of the lecture.

And if you have any questions to her, she'll be around after the lecture and also in the upcoming weeks.

Thank you, Maria. Nice to have you here.

You just arrived during the long weekend and now you are going to be a part of this team for the next four months.

That's fantastic.

All right. So content. What did we do last week?

A little bit of a recap. I hope you remember talking about the electrocardiogram and the electroencephalogram.

So in the electrocardiogram, we talked about what the ECG does. So the electrocardiogram measures heart activity.

And by the way, when I ask you in an oral exam or even in the written exam what biosignals you remember and you say ECG,

then maybe it's a good idea if you mention it for the first time that you actually know what it means.

So it's the electrocardiogram.

We also talked a little bit about the anatomy of the heart because it's relevant for signal generation.

So you should have at least a basic understanding of how the heart muscle is oriented in the human body in order to also understand the signals that are generated.

Because depending on whether you use the lead two of the ECG or whether you use the chest lead V5, for example,

you get a different projection of the electrical signal vector of the heart.

And that's relevant for analysis because if, for example, you look at pathological signal changes,

so when somebody has a myocardial infarction, it is relevant which of these leads you're looking at,

which of these channels of the standard 12-channel ECG.

And cardiologists work with that every day, so they know how to read and interpret these signals.

Maria and Stefan and a couple of the team members that I have,

they are working on translating this clinical knowledge into automatic decision-making processes.

So one of the goals of Maria's research today here could, for example, be to identify atrial fibrillation in the ECG.

And you want to do that automatically, and you want to do that in a way that is always the same wherever you use that.

Because that's different in clinical practice.

In clinical practice, you have three cardiologists look at the same 12-channel ECG,

and you will get three different opinions, probably.